diff --git a/01_Welcome.ipynb b/01_Welcome.ipynb
index 7161666b79b40df5e378606e31c446bd5304ec63..f8b29b15bb90b25ac0058e85c88851ec53ded14f 100644
--- a/01_Welcome.ipynb
+++ b/01_Welcome.ipynb
@@ -118,7 +118,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/02_Introduction_to_Tensorflow.ipynb b/02_Introduction_to_Tensorflow.ipynb
index 0593db28c4d8958ba2d05196b83ad3bcf5e9310a..4a7ef27feda3def589379aeb5f4daabb251819f2 100644
--- a/02_Introduction_to_Tensorflow.ipynb
+++ b/02_Introduction_to_Tensorflow.ipynb
@@ -1883,7 +1883,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/03_Introduction_to_Tensorflow_exercises.ipynb b/03_Introduction_to_Tensorflow_exercises.ipynb
index da625b4d4bba3030d45e6ec8d209503e626fe3f0..c2a86563212d1c65b8c9c0584b6e037506161cd0 100644
--- a/03_Introduction_to_Tensorflow_exercises.ipynb
+++ b/03_Introduction_to_Tensorflow_exercises.ipynb
@@ -854,7 +854,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/04_NN_regression_in_tensorflow.ipynb b/04_NN_regression_in_tensorflow.ipynb
index 7b019b61031daadf13b4bd82f287c20e5f38209d..3daba57dc791f710edbb6e2c774f2da8f8937230 100644
--- a/04_NN_regression_in_tensorflow.ipynb
+++ b/04_NN_regression_in_tensorflow.ipynb
@@ -1427,6 +1427,13 @@
"source": [
"From our experiments, it looks like `model_3` performed the best."
]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
}
],
"metadata": {
@@ -1454,7 +1461,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/05_NN_regression_in_tensorflow_exercise.ipynb b/05_NN_regression_in_tensorflow_exercise.ipynb
index 25bddd4d4ad399caf55c2d88050614ef3581f0c0..3a5296d657625401e8b7ee9503c587d811b2737e 100644
--- a/05_NN_regression_in_tensorflow_exercise.ipynb
+++ b/05_NN_regression_in_tensorflow_exercise.ipynb
@@ -557,7 +557,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/06_Regression_larger_example.ipynb b/06_Regression_larger_example.ipynb
index 62e78efbc4987d607baa63015fe5f7b8a60f17a5..64871d5955183c21fde666aa8bc16c9f3bf4d8c5 100644
--- a/06_Regression_larger_example.ipynb
+++ b/06_Regression_larger_example.ipynb
@@ -583,7 +583,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/07_Preprocessing_evaluation.ipynb b/07_Preprocessing_evaluation.ipynb
index 00a061a30def0ff02b625103d3af9b8910d52744..947eb915d1082fa6c2884ec00f90611974929534 100644
--- a/07_Preprocessing_evaluation.ipynb
+++ b/07_Preprocessing_evaluation.ipynb
@@ -700,7 +700,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/09_Image_classification.ipynb b/09_Image_classification.ipynb
index 1efec4ceeeb1b2e80156507243a02ea4c8727d7e..7c4feceaee5b9cfb2b69745bd548889957cf9e95 100644
--- a/09_Image_classification.ipynb
+++ b/09_Image_classification.ipynb
@@ -17,7 +17,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {
"tags": []
},
@@ -37,33 +37,81 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "**Author**: Yann LeCun, Corinna Cortes, Christopher J.C. Burges \n",
+ "**Source**: [MNIST Website](http://yann.lecun.com/exdb/mnist/) - Date unknown \n",
+ "**Please cite**: \n",
+ "\n",
+ "The MNIST database of handwritten digits with 784 features, raw data available at: http://yann.lecun.com/exdb/mnist/. It can be split in a training set of the first 60,000 examples, and a test set of 10,000 examples \n",
+ "\n",
+ "It is a subset of a larger set available from NIST. The digits have been size-normalized and centered in a fixed-size image. It is a good database for people who want to try learning techniques and pattern recognition methods on real-world data while spending minimal efforts on preprocessing and formatting. The original black and white (bilevel) images from NIST were size normalized to fit in a 20x20 pixel box while preserving their aspect ratio. The resulting images contain grey levels as a result of the anti-aliasing technique used by the normalization algorithm. the images were centered in a 28x28 image by computing the center of mass of the pixels, and translating the image so as to position this point at the center of the 28x28 field. \n",
+ "\n",
+ "With some classification methods (particularly template-based methods, such as SVM and K-nearest neighbors), the error rate improves when the digits are centered by bounding box rather than center of mass. If you do this kind of pre-processing, you should report it in your publications. The MNIST database was constructed from NIST's NIST originally designated SD-3 as their training set and SD-1 as their test set. However, SD-3 is much cleaner and easier to recognize than SD-1. The reason for this can be found on the fact that SD-3 was collected among Census Bureau employees, while SD-1 was collected among high-school students. Drawing sensible conclusions from learning experiments requires that the result be independent of the choice of training set and test among the complete set of samples. Therefore it was necessary to build a new database by mixing NIST's datasets. \n",
+ "\n",
+ "The MNIST training set is composed of 30,000 patterns from SD-3 and 30,000 patterns from SD-1. Our test set was composed of 5,000 patterns from SD-3 and 5,000 patterns from SD-1. The 60,000 pattern training set contained examples from approximately 250 writers. We made sure that the sets of writers of the training set and test set were disjoint. SD-1 contains 58,527 digit images written by 500 different writers. In contrast to SD-3, where blocks of data from each writer appeared in sequence, the data in SD-1 is scrambled. Writer identities for SD-1 is available and we used this information to unscramble the writers. We then split SD-1 in two: characters written by the first 250 writers went into our new training set. The remaining 250 writers were placed in our test set. Thus we had two sets with nearly 30,000 examples each. The new training set was completed with enough examples from SD-3, starting at pattern # 0, to make a full set of 60,000 training patterns. Similarly, the new test set was completed with SD-3 examples starting at pattern # 35,000 to make a full set with 60,000 test patterns. Only a subset of 10,000 test images (5,000 from SD-1 and 5,000 from SD-3) is available on this site. The full 60,000 sample training set is available.\n",
+ "\n",
+ "Downloaded from openml.org.\n"
+ ]
+ }
+ ],
"source": [
"print(mnist.DESCR)"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dict_keys(['data', 'target', 'frame', 'categories', 'feature_names', 'target_names', 'DESCR', 'details', 'url'])"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"mnist.keys() # We only need images (data) and labels (target) in this notebook"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0, 0, 0, ..., 0, 0, 0],\n",
+ " [0, 0, 0, ..., 0, 0, 0],\n",
+ " [0, 0, 0, ..., 0, 0, 0],\n",
+ " ...,\n",
+ " [0, 0, 0, ..., 0, 0, 0],\n",
+ " [0, 0, 0, ..., 0, 0, 0],\n",
+ " [0, 0, 0, ..., 0, 0, 0]])"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"X, y = mnist.data, mnist.target\n",
"X"
@@ -78,33 +126,66 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(70000, 784)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"X.shape"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array(['5', '0', '4', ..., '4', '5', '6'], dtype=object)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"y"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(70000,)"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"y.shape"
]
@@ -118,9 +199,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "784"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"28 * 28"
]
@@ -134,7 +226,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"metadata": {
"tags": []
},
@@ -152,7 +244,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"metadata": {
"tags": []
},
@@ -169,11 +261,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 300x300 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"def plot_digit(image_data):\n",
" image = image_data.reshape(28, 28)\n",
@@ -194,18 +297,29 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "'5'"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"y[0]"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"metadata": {
"tags": []
},
@@ -225,11 +339,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": "iVBORw0KGgoAAAANSUhEUgAAAPoAAAD7CAYAAABDsImYAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjguNCwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8fJSN1AAAACXBIWXMAAA9hAAAPYQGoP6dpAAAFsklEQVR4nO3dsUvVaxzH8dPtGoougTiVgxAknMGgJje3hgjBWRAi3KSxqcU/oJbGpqCivSYRpdmhthTEOE2mtQgS5Ln7HZ7nev15jp7P67V+f57nWd48w/M7xyvdbrfbAgbaX/3eAHD+hA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4BhA4B/u73BricDg4OivM3b94U5y9evKiucXh4WJxvbGwU5+12u7pGCic6BBA6BBA6BBA6BBA6BBA6BBA6BBA6BLjS7Xa7/d4EvXVyclJ95vXr18X56upqcb69vX2qPf0fc3Nzxfna2tq57+GycKJDAKFDAKFDAKFDAKFDAKFDAKFDAD88MYD29vaK88XFxepnbG5uNrUdLgAnOgQQOgQQOgQQOgQQOgQQOgQQOgRwj34B1b4vvr6+XpwvLy8X5zs7O6feU9Nu3LhRfeb4+Lg4Hxsba2o7A8+JDgGEDgGEDgGEDgGEDgGEDgGEDgHco/fYnz9/qs98/PixOH/w4EFT2+mbu3fvVp959epVcT46OtrUdgaeEx0CCB0CCB0CCB0CCB0CCB0CCB0CCB0CeGGmYbUXYmovw7Rag/FCTM309HT1mevXr/dgJxmc6BBA6BBA6BBA6BBA6BBA6BBA6BDAPXrDOp1OcZ5wR95qtVrXrl0rzqempnq0E1otJzpEEDoEEDoEEDoEEDoEEDoEEDoEcI/esPfv3/d7CxfCzMxMcf7o0aPebIRWq+VEhwhChwBChwBChwBChwBChwBChwDu0Rv24cOHfm+hanx8vPrMyclJcX54eFicj46OnmpPnC8nOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgTwwkzDFhYWivP19fUzr9Fut4vzp0+fFueTk5PVNebn50+1p3+7f//+mf6eZjnRIYDQIYDQIYDQIYDQIYDQIYDQIYB79IY9fvy4OL9582b1M46Ojorzhw8fFucjIyPF+bNnz6p7+PHjR3E+PDxcnN+5c6e6Br3jRIcAQocAQocAQocAQocAQocAQocAV7rdbrffm6BZu7u7xfnt27ern/H79+/ifGVlpTh//vx5dQ16x4kOAYQOAYQOAYQOAYQOAYQOAYQOAdyjX0LHx8fF+dLSUnH+9u3b6hq1u/bPnz8X50NDQ9U16B0nOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgTwDxwuoS9fvhTn/+WFmJp79+4V516IuVyc6BBA6BBA6BBA6BBA6BBA6BBA6BDAD09cQD9//izOZ2ZmivNv374V57du3aruYWtrqzgfGxurfgYXhxMdAggdAggdAggdAggdAggdAggdAvg++gW0sbFRnNfuyWuWl5erz7gnHyxOdAggdAggdAggdAggdAggdAggdAjg++g99uvXr+oz7Xa7OP/+/XtxPjU1VZx//fq1uoerV69Wn+HycKJDAKFDAKFDAKFDAKFDAKFDAKFDAKFDAD880WPv3r2rPlN7IWZ4eLg4f/nyZXHuZZg8TnQIIHQIIHQIIHQIIHQIIHQIIHQI4IcnGra/v1+cT0xMnHmN2dnZ4vzTp09nXoPB4kSHAEKHAEKHAEKHAEKHAEKHAEKHAL6P3rBOp3Puazx58uTc12CwONEhgNAhgNAhgNAhgNAhgNAhgNAhgO+jQwAnOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgQQOgT4B8gbvUNZJLkfAAAAAElFTkSuQmCC",
+ "text/plain": [
+ "<Figure size 300x300 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"some_digit_number = np.random.randint(0,69999)\n",
"some_digit = X[some_digit_number]\n",
@@ -245,7 +370,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"metadata": {
"tags": []
},
@@ -256,7 +381,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"metadata": {
"tags": []
},
@@ -267,11 +392,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((56000, 784), (56000,), (14000, 784), (14000,))"
+ ]
+ },
+ "execution_count": 17,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"X_train.shape, y_train.shape, X_test.shape, y_test.shape"
]
@@ -285,11 +421,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((56000, 28, 28), (14000, 28, 28))"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"X_train = X_train.reshape(56000,28,28)\n",
"X_test = X_test.reshape(14000,28,28)\n",
@@ -298,11 +445,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 300x300 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"plot_digit(X_train[0])"
]
@@ -318,18 +476,29 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(255, 0, 255, 0)"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"X_train.max(), X_train.min(), X_test.max(), X_test.min()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"metadata": {
"tags": []
},
@@ -340,22 +509,45 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(1.0, 0.0, 1.0, 0.0)"
+ ]
+ },
+ "execution_count": 22,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"X_train.max(), X_train.min(), X_test.max(), X_test.min()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-03-31 14:04:56.016192: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+ "2025-03-31 14:04:56.016290: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+ "2025-03-31 14:04:56.017047: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+ "2025-03-31 14:04:56.022499: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+ ]
+ }
+ ],
"source": [
"import tensorflow as tf"
]
@@ -375,29 +567,51 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 24,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(56000, 28, 28)"
+ ]
+ },
+ "execution_count": 24,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"X_train.shape"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 25,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(dtype('float64'), dtype('O'))"
+ ]
+ },
+ "execution_count": 25,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"X_train.dtype, y_train.dtype"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 26,
"metadata": {
"tags": []
},
@@ -408,7 +622,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 27,
"metadata": {
"tags": []
},
@@ -419,18 +633,30 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 28,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(array([4, 9, 5, ..., 1, 6, 3], dtype=int32),\n",
+ " array([2, 8, 0, ..., 2, 9, 3], dtype=int32))"
+ ]
+ },
+ "execution_count": 28,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"y_train, y_test"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 29,
"metadata": {
"tags": []
},
@@ -441,11 +667,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 30,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-03-31 14:04:58.306114: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1022 MB memory: -> device: 0, name: NVIDIA A40, pci bus id: 0000:41:00.0, compute capability: 8.6\n"
+ ]
+ }
+ ],
"source": [
"model_1 = tf.keras.models.Sequential([\n",
" tf.keras.layers.Flatten(input_shape=(28, 28)),\n",
@@ -457,7 +691,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 31,
"metadata": {
"tags": []
},
@@ -470,20 +704,89 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 32,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/5\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-03-31 14:04:59.721859: I external/local_xla/xla/service/service.cc:168] XLA service 0x7f3515fbc4c0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
+ "2025-03-31 14:04:59.721933: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA A40, Compute Capability 8.6\n",
+ "2025-03-31 14:04:59.727321: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
+ "2025-03-31 14:04:59.747618: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8907\n",
+ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+ "I0000 00:00:1743422699.846413 2630827 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1750/1750 [==============================] - 4s 2ms/step - loss: 0.2998 - accuracy: 0.9131 - val_loss: 0.1559 - val_accuracy: 0.9544\n",
+ "Epoch 2/5\n",
+ "1750/1750 [==============================] - 3s 2ms/step - loss: 0.1421 - accuracy: 0.9578 - val_loss: 0.1211 - val_accuracy: 0.9644\n",
+ "Epoch 3/5\n",
+ "1750/1750 [==============================] - 3s 2ms/step - loss: 0.1049 - accuracy: 0.9683 - val_loss: 0.1047 - val_accuracy: 0.9698\n",
+ "Epoch 4/5\n",
+ "1750/1750 [==============================] - 3s 2ms/step - loss: 0.0875 - accuracy: 0.9726 - val_loss: 0.0936 - val_accuracy: 0.9734\n",
+ "Epoch 5/5\n",
+ "1750/1750 [==============================] - 3s 2ms/step - loss: 0.0727 - accuracy: 0.9771 - val_loss: 0.0878 - val_accuracy: 0.9754\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "<keras.src.callbacks.History at 0x7f36480f2a10>"
+ ]
+ },
+ "execution_count": 32,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"model_1.fit(X_train, y_train, epochs=5, validation_data=(X_test, y_test))"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 33,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " flatten (Flatten) (None, 784) 0 \n",
+ " \n",
+ " dense (Dense) (None, 128) 100480 \n",
+ " \n",
+ " dropout (Dropout) (None, 128) 0 \n",
+ " \n",
+ " dense_1 (Dense) (None, 10) 1290 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 101770 (397.54 KB)\n",
+ "Trainable params: 101770 (397.54 KB)\n",
+ "Non-trainable params: 0 (0.00 Byte)\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
"source": [
"model_1.summary()"
]
@@ -504,11 +807,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 34,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "438/438 [==============================] - 0s 681us/step\n"
+ ]
+ }
+ ],
"source": [
"y_pred = model_1.predict(X_test)"
]
@@ -523,18 +834,41 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 35,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[6.76042772e-13, 2.74699031e-11, 9.99998093e-01, ...,\n",
+ " 5.49174617e-09, 5.49645174e-09, 4.30519856e-12],\n",
+ " [7.26928349e-08, 5.27950624e-06, 1.54388302e-09, ...,\n",
+ " 4.18459640e-05, 9.02112722e-01, 4.96554363e-04],\n",
+ " [9.96783018e-01, 1.00510285e-08, 2.84118345e-03, ...,\n",
+ " 4.44693562e-08, 2.91404780e-04, 9.96766630e-07],\n",
+ " ...,\n",
+ " [2.96980191e-15, 1.35357325e-07, 9.99990463e-01, ...,\n",
+ " 2.08023754e-09, 1.06622203e-07, 1.26931064e-15],\n",
+ " [3.17074296e-06, 9.51665982e-08, 1.18144699e-05, ...,\n",
+ " 3.12783867e-02, 5.91487014e-06, 9.68266964e-01],\n",
+ " [2.31753366e-11, 1.04931701e-07, 3.26574735e-07, ...,\n",
+ " 4.66864603e-10, 2.52760492e-06, 1.55797136e-06]], dtype=float32)"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"y_pred"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 36,
"metadata": {
"tags": []
},
@@ -545,22 +879,44 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 37,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "<tf.Tensor: shape=(14000,), dtype=int64, numpy=array([2, 8, 0, ..., 2, 9, 3])>"
+ ]
+ },
+ "execution_count": 37,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"y_preds"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 38,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([2, 8, 0, ..., 2, 9, 3], dtype=int32)"
+ ]
+ },
+ "execution_count": 38,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"y_test"
]
@@ -575,7 +931,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 39,
"metadata": {
"tags": []
},
@@ -586,11 +942,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 40,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"plt.rc('font', size=9) # make the text smaller\n",
"ConfusionMatrixDisplay.from_predictions(y_test, y_preds)\n",
@@ -606,11 +973,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 41,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"plt.rc('font', size=10) # extra code\n",
"ConfusionMatrixDisplay.from_predictions(y_test, y_preds,\n",
@@ -627,11 +1005,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 42,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"sample_weight = (y_preds != y_test)\n",
"plt.rc('font', size=10) # extra code\n",
@@ -650,9 +1039,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 43,
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"sample_weight = (y_preds != y_test)\n",
"plt.rc('font', size=10) # extra code\n",
@@ -686,7 +1086,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
},
"nav_menu": {},
"toc": {
diff --git a/10_Image_classification_exercise.ipynb b/10_Image_classification_exercise.ipynb
index 93f0e65202783383bb012c232794a1585949eb35..1fdf689d0e6ade82ca3ac34ffa9e90a5d12e6d66 100644
--- a/10_Image_classification_exercise.ipynb
+++ b/10_Image_classification_exercise.ipynb
@@ -557,7 +557,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/11_Image_classification_solution.ipynb b/11_Image_classification_solution.ipynb
index 16a0dd82208fe7a8453ef76475caa979855c3af4..c8113737098949caa881195dd0bd2c6edf790b2a 100644
--- a/11_Image_classification_solution.ipynb
+++ b/11_Image_classification_solution.ipynb
@@ -15,7 +15,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "1d6e0ec9-a0f9-4219-ae57-b0d2d7b28cbb",
"metadata": {
"colab": {
@@ -25,7 +25,19 @@
"outputId": "a84d56e1-3caa-4fe3-da3d-daa957de8bb8",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-03-31 14:06:08.287110: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+ "2025-03-31 14:06:08.287193: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+ "2025-03-31 14:06:08.288033: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+ "2025-03-31 14:06:08.294293: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+ ]
+ }
+ ],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
@@ -47,7 +59,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "d48cf683-913f-49b4-9205-f61c527d82f1",
"metadata": {
"colab": {
@@ -57,7 +69,18 @@
"outputId": "875e43f8-6f45-4de7-a690-3e5f22dc9346",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Check the shape of your data\n",
"train_data.shape, train_labels.shape, test_data.shape, test_labels.shape"
@@ -65,7 +88,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"id": "09cb4b21-3277-4fa9-9160-8506ea0837ce",
"metadata": {
"colab": {
@@ -75,7 +98,18 @@
"outputId": "eb29775a-9247-4b6f-f635-aa14a4152fa1",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(28, 28)"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Look at the shape of a single example\n",
"train_data[0].shape"
@@ -103,7 +137,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"id": "090cc953-6b36-4312-937f-c6e8145ca686",
"metadata": {
"colab": {
@@ -114,7 +148,28 @@
"outputId": "90805579-22f4-410a-e140-8051a20665e8",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(<matplotlib.image.AxesImage at 0x7f4027d91e40>, 38346)"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": "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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# Plot a random example\n",
"random_image_number = np.random.randint(0, 59999)\n",
@@ -133,7 +188,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"id": "7969a36a-6353-4520-ab5e-af824de4c0da",
"metadata": {
"colab": {
@@ -143,7 +198,18 @@
"outputId": "e436486f-eab2-4327-e8d0-13e6a6e78f99",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "4"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Check the random sample's label\n",
"train_labels[random_image_number]"
@@ -163,7 +229,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"id": "ed2f2bc8-388a-48a8-b033-bc977e731dc9",
"metadata": {
"colab": {
@@ -189,7 +255,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "db5271aa-a87f-41d2-a6c5-36419e63480d",
"metadata": {
"colab": {
@@ -199,7 +265,18 @@
"outputId": "c585fda2-b5c2-4c34-b0dd-85fd5d24ecae",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# How many classes are there?\n",
"len(class_names)"
@@ -215,19 +292,30 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"id": "8f5d4edc-64c3-4103-ac0f-0a57fa8cfd8d",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "255"
+ ]
+ },
+ "execution_count": 8,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"train_data.max()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"id": "05f598ce-88b3-42c0-add0-7d94c94c0777",
"metadata": {
"colab": {
@@ -237,7 +325,53 @@
"outputId": "1cf10905-3f65-433d-cf34-fe0cc3f26158",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-03-31 14:06:11.308814: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1022 MB memory: -> device: 0, name: NVIDIA A40, pci bus id: 0000:41:00.0, compute capability: 8.6\n",
+ "2025-03-31 14:06:11.992883: I external/local_xla/xla/service/service.cc:168] XLA service 0x7f3e243fdae0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
+ "2025-03-31 14:06:11.992958: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA A40, Compute Capability 8.6\n",
+ "2025-03-31 14:06:11.998918: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
+ "2025-03-31 14:06:12.018468: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8907\n",
+ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+ "I0000 00:00:1743422772.116016 2631387 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 2.3649 - accuracy: 0.1009 - val_loss: 2.2668 - val_accuracy: 0.1175\n",
+ "Epoch 2/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 2.1612 - accuracy: 0.1490 - val_loss: 1.8877 - val_accuracy: 0.2068\n",
+ "Epoch 3/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 1.8038 - accuracy: 0.2279 - val_loss: 1.7366 - val_accuracy: 0.2446\n",
+ "Epoch 4/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 1.7284 - accuracy: 0.2499 - val_loss: 1.6889 - val_accuracy: 0.2691\n",
+ "Epoch 5/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 1.6993 - accuracy: 0.2586 - val_loss: 1.6796 - val_accuracy: 0.2648\n",
+ "Epoch 6/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 1.6851 - accuracy: 0.2584 - val_loss: 1.6609 - val_accuracy: 0.2611\n",
+ "Epoch 7/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 1.6678 - accuracy: 0.2634 - val_loss: 1.6644 - val_accuracy: 0.2496\n",
+ "Epoch 8/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 1.6696 - accuracy: 0.2605 - val_loss: 1.6438 - val_accuracy: 0.2600\n",
+ "Epoch 9/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 1.6610 - accuracy: 0.2614 - val_loss: 1.6822 - val_accuracy: 0.2694\n",
+ "Epoch 10/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 1.6637 - accuracy: 0.2621 - val_loss: 1.6423 - val_accuracy: 0.2877\n"
+ ]
+ }
+ ],
"source": [
"# Set random seed\n",
"tf.random.set_seed(42)\n",
@@ -272,7 +406,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"id": "af3c9db1-5991-4b7b-bd27-8568ed280128",
"metadata": {
"colab": {
@@ -282,7 +416,31 @@
"outputId": "6c30b8af-5f19-4c4c-c65e-68436f8d87d7",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " flatten (Flatten) (None, 784) 0 \n",
+ " \n",
+ " dense (Dense) (None, 4) 3140 \n",
+ " \n",
+ " dense_1 (Dense) (None, 4) 20 \n",
+ " \n",
+ " dense_2 (Dense) (None, 10) 50 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 3210 (12.54 KB)\n",
+ "Trainable params: 3210 (12.54 KB)\n",
+ "Non-trainable params: 0 (0.00 Byte)\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
"source": [
"model_1.summary()"
]
@@ -299,7 +457,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"id": "9e633182-deb2-4a9b-bb2e-86b060d527cb",
"metadata": {
"colab": {
@@ -309,7 +467,18 @@
"outputId": "334ad24e-1c94-4973-8b4c-3e3c1eef7bc7",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0, 255)"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Check the min and max values of the training data\n",
"train_data.min(), train_data.max()"
@@ -317,7 +486,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"id": "70830f10-7796-40b7-9622-75dbe13c6524",
"metadata": {
"colab": {
@@ -327,7 +496,18 @@
"outputId": "5443c261-26ad-49f8-cf36-37d259224037",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.0, 1.0)"
+ ]
+ },
+ "execution_count": 12,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Divide train and test images by the maximum value (normalize it)\n",
"train_data = train_data / 255.0\n",
@@ -339,7 +519,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "6d656976-f555-43d6-9e18-8cbc1005b106",
"metadata": {
"colab": {
@@ -349,7 +529,34 @@
"outputId": "340beb7f-f094-4225-b8a4-0422aab83c6c",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 1.2956 - accuracy: 0.4770 - val_loss: 0.9963 - val_accuracy: 0.6534\n",
+ "Epoch 2/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.8096 - accuracy: 0.7145 - val_loss: 0.7361 - val_accuracy: 0.7478\n",
+ "Epoch 3/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6964 - accuracy: 0.7546 - val_loss: 0.6911 - val_accuracy: 0.7537\n",
+ "Epoch 4/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6666 - accuracy: 0.7637 - val_loss: 0.6758 - val_accuracy: 0.7622\n",
+ "Epoch 5/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6526 - accuracy: 0.7652 - val_loss: 0.6704 - val_accuracy: 0.7641\n",
+ "Epoch 6/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6417 - accuracy: 0.7704 - val_loss: 0.6703 - val_accuracy: 0.7605\n",
+ "Epoch 7/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6351 - accuracy: 0.7711 - val_loss: 0.6649 - val_accuracy: 0.7639\n",
+ "Epoch 8/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6290 - accuracy: 0.7740 - val_loss: 0.6556 - val_accuracy: 0.7692\n",
+ "Epoch 9/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6247 - accuracy: 0.7753 - val_loss: 0.6492 - val_accuracy: 0.7725\n",
+ "Epoch 10/10\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6205 - accuracy: 0.7764 - val_loss: 0.6462 - val_accuracy: 0.7742\n"
+ ]
+ }
+ ],
"source": [
"# Set random seed\n",
"tf.random.set_seed(42)\n",
@@ -386,7 +593,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"id": "f17cbb86-35a8-4353-9ab0-5ad7f232a288",
"metadata": {
"colab": {
@@ -397,7 +604,28 @@
"outputId": "16b69ea0-3b10-4280-eedc-af4c1bd7493a",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import pandas as pd\n",
"# Plot non-normalized data loss curves\n",
@@ -418,7 +646,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "a50376df-20dd-4d1f-b925-d403a52b0e91",
"metadata": {
"colab": {
@@ -428,7 +656,94 @@
"outputId": "53b1f56a-ac02-4039-dd6a-d12165886022",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/40\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 1.4256 - accuracy: 0.4848 - val_loss: 1.1201 - val_accuracy: 0.6015 - lr: 0.0010\n",
+ "Epoch 2/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 1.0077 - accuracy: 0.6316 - val_loss: 0.9290 - val_accuracy: 0.6614 - lr: 0.0011\n",
+ "Epoch 3/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.8421 - accuracy: 0.7013 - val_loss: 0.7745 - val_accuracy: 0.7296 - lr: 0.0013\n",
+ "Epoch 4/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6864 - accuracy: 0.7722 - val_loss: 0.6877 - val_accuracy: 0.7764 - lr: 0.0014\n",
+ "Epoch 5/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6261 - accuracy: 0.7914 - val_loss: 0.6304 - val_accuracy: 0.7894 - lr: 0.0016\n",
+ "Epoch 6/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5992 - accuracy: 0.7968 - val_loss: 0.6358 - val_accuracy: 0.7897 - lr: 0.0018\n",
+ "Epoch 7/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5857 - accuracy: 0.7990 - val_loss: 0.6555 - val_accuracy: 0.7737 - lr: 0.0020\n",
+ "Epoch 8/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5765 - accuracy: 0.8005 - val_loss: 0.5956 - val_accuracy: 0.7996 - lr: 0.0022\n",
+ "Epoch 9/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5725 - accuracy: 0.8026 - val_loss: 0.6376 - val_accuracy: 0.7815 - lr: 0.0025\n",
+ "Epoch 10/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5681 - accuracy: 0.8041 - val_loss: 0.6773 - val_accuracy: 0.7648 - lr: 0.0028\n",
+ "Epoch 11/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5646 - accuracy: 0.8050 - val_loss: 0.5982 - val_accuracy: 0.7975 - lr: 0.0032\n",
+ "Epoch 12/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5625 - accuracy: 0.8053 - val_loss: 0.6439 - val_accuracy: 0.7850 - lr: 0.0035\n",
+ "Epoch 13/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5640 - accuracy: 0.8044 - val_loss: 0.6150 - val_accuracy: 0.7924 - lr: 0.0040\n",
+ "Epoch 14/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5628 - accuracy: 0.8043 - val_loss: 0.5979 - val_accuracy: 0.7954 - lr: 0.0045\n",
+ "Epoch 15/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5710 - accuracy: 0.8033 - val_loss: 0.6034 - val_accuracy: 0.7890 - lr: 0.0050\n",
+ "Epoch 16/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5709 - accuracy: 0.8022 - val_loss: 0.5962 - val_accuracy: 0.7990 - lr: 0.0056\n",
+ "Epoch 17/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5706 - accuracy: 0.8020 - val_loss: 0.5887 - val_accuracy: 0.7999 - lr: 0.0063\n",
+ "Epoch 18/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5719 - accuracy: 0.8012 - val_loss: 0.6046 - val_accuracy: 0.7954 - lr: 0.0071\n",
+ "Epoch 19/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5766 - accuracy: 0.7983 - val_loss: 0.6147 - val_accuracy: 0.7874 - lr: 0.0079\n",
+ "Epoch 20/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5807 - accuracy: 0.7993 - val_loss: 0.5934 - val_accuracy: 0.7983 - lr: 0.0089\n",
+ "Epoch 21/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5837 - accuracy: 0.7980 - val_loss: 0.6693 - val_accuracy: 0.7669 - lr: 0.0100\n",
+ "Epoch 22/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5867 - accuracy: 0.7964 - val_loss: 0.6254 - val_accuracy: 0.7889 - lr: 0.0112\n",
+ "Epoch 23/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5978 - accuracy: 0.7937 - val_loss: 0.6096 - val_accuracy: 0.7920 - lr: 0.0126\n",
+ "Epoch 24/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6041 - accuracy: 0.7893 - val_loss: 0.6037 - val_accuracy: 0.7936 - lr: 0.0141\n",
+ "Epoch 25/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6060 - accuracy: 0.7900 - val_loss: 0.6516 - val_accuracy: 0.7890 - lr: 0.0158\n",
+ "Epoch 26/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6222 - accuracy: 0.7832 - val_loss: 0.7425 - val_accuracy: 0.7496 - lr: 0.0178\n",
+ "Epoch 27/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6322 - accuracy: 0.7799 - val_loss: 0.6999 - val_accuracy: 0.7687 - lr: 0.0200\n",
+ "Epoch 28/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6369 - accuracy: 0.7785 - val_loss: 0.6469 - val_accuracy: 0.7883 - lr: 0.0224\n",
+ "Epoch 29/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6596 - accuracy: 0.7716 - val_loss: 0.7371 - val_accuracy: 0.7587 - lr: 0.0251\n",
+ "Epoch 30/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6905 - accuracy: 0.7578 - val_loss: 0.7506 - val_accuracy: 0.7524 - lr: 0.0282\n",
+ "Epoch 31/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.7229 - accuracy: 0.7498 - val_loss: 0.6848 - val_accuracy: 0.7698 - lr: 0.0316\n",
+ "Epoch 32/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.7312 - accuracy: 0.7478 - val_loss: 0.8226 - val_accuracy: 0.6988 - lr: 0.0355\n",
+ "Epoch 33/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.7816 - accuracy: 0.7031 - val_loss: 0.8869 - val_accuracy: 0.6347 - lr: 0.0398\n",
+ "Epoch 34/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.8183 - accuracy: 0.6848 - val_loss: 0.7943 - val_accuracy: 0.6367 - lr: 0.0447\n",
+ "Epoch 35/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.8398 - accuracy: 0.6805 - val_loss: 0.7365 - val_accuracy: 0.7054 - lr: 0.0501\n",
+ "Epoch 36/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.9801 - accuracy: 0.6316 - val_loss: 0.9395 - val_accuracy: 0.6614 - lr: 0.0562\n",
+ "Epoch 37/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 1.0243 - accuracy: 0.6210 - val_loss: 1.0161 - val_accuracy: 0.5786 - lr: 0.0631\n",
+ "Epoch 38/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 1.0519 - accuracy: 0.6115 - val_loss: 1.0159 - val_accuracy: 0.6083 - lr: 0.0708\n",
+ "Epoch 39/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 1.1487 - accuracy: 0.5634 - val_loss: 1.0318 - val_accuracy: 0.6208 - lr: 0.0794\n",
+ "Epoch 40/40\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 1.5180 - accuracy: 0.3553 - val_loss: 1.8047 - val_accuracy: 0.1899 - lr: 0.0891\n"
+ ]
+ }
+ ],
"source": [
"# Set random seed\n",
"tf.random.set_seed(42)\n",
@@ -459,7 +774,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 16,
"id": "9b3cefb8-46a3-475a-830e-ccfc441f8da7",
"metadata": {
"colab": {
@@ -470,7 +785,18 @@
"outputId": "a43aa562-143f-450c-8371-c09a822d8a8a",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# Plot the learning rate decay curve\n",
"import numpy as np\n",
@@ -494,7 +820,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 17,
"id": "cbe902ac-597e-4f6e-8fd6-6199310bfa97",
"metadata": {
"colab": {
@@ -504,7 +830,54 @@
"outputId": "e846742a-83b8-4702-a0a4-7413391509a3",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 1.2507 - accuracy: 0.4947 - val_loss: 0.8581 - val_accuracy: 0.6860\n",
+ "Epoch 2/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.7283 - accuracy: 0.7416 - val_loss: 0.6860 - val_accuracy: 0.7554\n",
+ "Epoch 3/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6372 - accuracy: 0.7697 - val_loss: 0.6433 - val_accuracy: 0.7786\n",
+ "Epoch 4/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.6077 - accuracy: 0.7867 - val_loss: 0.6241 - val_accuracy: 0.7865\n",
+ "Epoch 5/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5909 - accuracy: 0.7956 - val_loss: 0.6215 - val_accuracy: 0.7849\n",
+ "Epoch 6/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5771 - accuracy: 0.8009 - val_loss: 0.6056 - val_accuracy: 0.7960\n",
+ "Epoch 7/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5675 - accuracy: 0.8044 - val_loss: 0.6059 - val_accuracy: 0.7932\n",
+ "Epoch 8/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5599 - accuracy: 0.8070 - val_loss: 0.5917 - val_accuracy: 0.7980\n",
+ "Epoch 9/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5516 - accuracy: 0.8120 - val_loss: 0.5972 - val_accuracy: 0.7949\n",
+ "Epoch 10/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5469 - accuracy: 0.8119 - val_loss: 0.5880 - val_accuracy: 0.7966\n",
+ "Epoch 11/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5433 - accuracy: 0.8132 - val_loss: 0.5844 - val_accuracy: 0.8040\n",
+ "Epoch 12/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5375 - accuracy: 0.8166 - val_loss: 0.5728 - val_accuracy: 0.8066\n",
+ "Epoch 13/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5345 - accuracy: 0.8169 - val_loss: 0.5734 - val_accuracy: 0.8070\n",
+ "Epoch 14/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5310 - accuracy: 0.8186 - val_loss: 0.5715 - val_accuracy: 0.8085\n",
+ "Epoch 15/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5279 - accuracy: 0.8194 - val_loss: 0.5659 - val_accuracy: 0.8107\n",
+ "Epoch 16/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.5243 - accuracy: 0.8213 - val_loss: 0.5673 - val_accuracy: 0.8101\n",
+ "Epoch 17/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5201 - accuracy: 0.8224 - val_loss: 0.5671 - val_accuracy: 0.8094\n",
+ "Epoch 18/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5193 - accuracy: 0.8227 - val_loss: 0.5614 - val_accuracy: 0.8118\n",
+ "Epoch 19/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5178 - accuracy: 0.8224 - val_loss: 0.5689 - val_accuracy: 0.8098\n",
+ "Epoch 20/20\n",
+ "1875/1875 [==============================] - 3s 2ms/step - loss: 0.5142 - accuracy: 0.8245 - val_loss: 0.5665 - val_accuracy: 0.8094\n"
+ ]
+ }
+ ],
"source": [
"# Set random seed\n",
"tf.random.set_seed(42)\n",
@@ -539,7 +912,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 18,
"id": "89444c74-51d7-4e82-ace9-fb85f48d8ccc",
"metadata": {
"colab": {
@@ -549,7 +922,39 @@
"outputId": "a3caac74-fdaf-4d21-8d50-01a1ca050d52",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "313/313 [==============================] - 0s 725us/step\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "array([[6.69220606e-14, 2.13247201e-37, 2.46736621e-16, 6.05273621e-21,\n",
+ " 1.09896590e-18, 3.22070211e-01, 1.71844370e-08, 1.43682733e-01,\n",
+ " 6.50328511e-05, 5.34182012e-01],\n",
+ " [1.03680352e-02, 3.49910022e-03, 7.79258609e-01, 3.08877695e-03,\n",
+ " 7.33830482e-02, 3.67364637e-03, 1.26053095e-01, 2.51379854e-04,\n",
+ " 4.22698620e-04, 1.67984831e-06],\n",
+ " [7.95959859e-05, 9.94242370e-01, 2.38429898e-08, 5.67147089e-03,\n",
+ " 6.35254855e-06, 2.21268560e-13, 1.41375892e-07, 1.01600685e-26,\n",
+ " 1.69118192e-12, 3.80468119e-23],\n",
+ " [1.80333736e-05, 9.97038484e-01, 1.24730670e-09, 2.94233928e-03,\n",
+ " 1.17712864e-06, 5.59693706e-15, 1.23681243e-08, 4.75620550e-30,\n",
+ " 7.02430057e-14, 8.14798652e-26],\n",
+ " [9.00774524e-02, 7.46683567e-04, 1.96030080e-01, 5.32079786e-02,\n",
+ " 5.98009787e-02, 6.86704600e-03, 5.81019163e-01, 3.73261150e-06,\n",
+ " 1.22454762e-02, 1.36780022e-06]], dtype=float32)"
+ ]
+ },
+ "execution_count": 18,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Make predictions with the most recent model\n",
"y_probs = model_4.predict(test_data) # \"probs\" is short for probabilities\n",
@@ -570,7 +975,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 19,
"id": "0a57ed57-b6b1-4f86-8113-afd12b2f9cfd",
"metadata": {
"colab": {
@@ -580,7 +985,18 @@
"outputId": "a9e1bca8-50fe-4973-dc4b-df5f67c874e7",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([9, 2, 1, 1, 6, 1, 4, 6, 5, 7])"
+ ]
+ },
+ "execution_count": 19,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Convert all of the predictions from probabilities to labels\n",
"y_preds = y_probs.argmax(axis=1)\n",
@@ -601,7 +1017,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 20,
"id": "e9c18174-99c6-4565-90a0-43c419a00c96",
"metadata": {
"colab": {
@@ -611,7 +1027,27 @@
"outputId": "8bdcc733-796d-4bdb-a5c8-62086c85eb01",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[747, 12, 24, 74, 3, 1, 128, 0, 11, 0],\n",
+ " [ 1, 952, 11, 22, 6, 0, 8, 0, 0, 0],\n",
+ " [ 18, 2, 682, 8, 190, 0, 99, 0, 1, 0],\n",
+ " [ 38, 29, 8, 790, 56, 2, 74, 0, 3, 0],\n",
+ " [ 0, 2, 115, 19, 794, 0, 66, 0, 4, 0],\n",
+ " [ 2, 0, 0, 0, 0, 889, 5, 61, 4, 39],\n",
+ " [142, 1, 146, 42, 160, 0, 491, 0, 18, 0],\n",
+ " [ 0, 0, 0, 0, 0, 33, 0, 947, 0, 20],\n",
+ " [ 5, 0, 2, 8, 7, 5, 76, 4, 893, 0],\n",
+ " [ 1, 0, 0, 0, 0, 20, 0, 70, 0, 909]])"
+ ]
+ },
+ "execution_count": 20,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Check out the non-prettified confusion matrix\n",
"from sklearn.metrics import confusion_matrix\n",
@@ -621,7 +1057,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 21,
"id": "d67c436c-e004-4208-8471-85b91fa44d2b",
"metadata": {
"tags": []
@@ -633,12 +1069,23 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"id": "f5611a2c-93c0-4b6f-82ea-e6382bfd6b7c",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 2 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"plt.rc('font', size=10) # extra code\n",
"ConfusionMatrixDisplay.from_predictions(test_labels, y_preds,\n",
@@ -656,12 +1103,20 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"id": "568109c5-26c5-4332-86a1-f8e21db8199f",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "['T-shirt/top', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']\n"
+ ]
+ }
+ ],
"source": [
"print(class_names)"
]
@@ -691,7 +1146,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/12_CNN.ipynb b/12_CNN.ipynb
index 2c26dd4e9ea458b3269cf1a659ed054cd6179872..d46fee9904bacdb0d753c09a3eeaaee5a6992ccb 100644
--- a/12_CNN.ipynb
+++ b/12_CNN.ipynb
@@ -15,7 +15,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"id": "1d6e0ec9-a0f9-4219-ae57-b0d2d7b28cbb",
"metadata": {
"colab": {
@@ -25,7 +25,19 @@
"outputId": "a84d56e1-3caa-4fe3-da3d-daa957de8bb8",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-03-31 14:13:19.481225: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+ "2025-03-31 14:13:19.481319: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+ "2025-03-31 14:13:19.482135: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+ "2025-03-31 14:13:19.488373: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
+ ]
+ }
+ ],
"source": [
"import tensorflow as tf\n",
"import numpy as np\n",
@@ -47,7 +59,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 2,
"id": "d48cf683-913f-49b4-9205-f61c527d82f1",
"metadata": {
"colab": {
@@ -57,7 +69,18 @@
"outputId": "875e43f8-6f45-4de7-a690-3e5f22dc9346",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "((60000, 28, 28), (60000,), (10000, 28, 28), (10000,))"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Check the shape of your data\n",
"train_data.shape, train_labels.shape, test_data.shape, test_labels.shape"
@@ -65,7 +88,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 3,
"id": "09cb4b21-3277-4fa9-9160-8506ea0837ce",
"metadata": {
"colab": {
@@ -75,7 +98,18 @@
"outputId": "eb29775a-9247-4b6f-f635-aa14a4152fa1",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(28, 28)"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Look at the shape of a single example\n",
"train_data[0].shape"
@@ -103,7 +137,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 4,
"id": "090cc953-6b36-4312-937f-c6e8145ca686",
"metadata": {
"colab": {
@@ -114,7 +148,28 @@
"outputId": "90805579-22f4-410a-e140-8051a20665e8",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(<matplotlib.image.AxesImage at 0x7fd35fd49e10>, 39067)"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ },
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"# Plot a random example\n",
"random_image_number = np.random.randint(0, 59999)\n",
@@ -133,7 +188,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 5,
"id": "7969a36a-6353-4520-ab5e-af824de4c0da",
"metadata": {
"colab": {
@@ -143,7 +198,18 @@
"outputId": "e436486f-eab2-4327-e8d0-13e6a6e78f99",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "2"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Check the random sample's label\n",
"train_labels[random_image_number]"
@@ -163,7 +229,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 6,
"id": "ed2f2bc8-388a-48a8-b033-bc977e731dc9",
"metadata": {
"colab": {
@@ -189,7 +255,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 7,
"id": "db5271aa-a87f-41d2-a6c5-36419e63480d",
"metadata": {
"colab": {
@@ -199,7 +265,18 @@
"outputId": "c585fda2-b5c2-4c34-b0dd-85fd5d24ecae",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "10"
+ ]
+ },
+ "execution_count": 7,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# How many classes are there?\n",
"len(class_names)"
@@ -215,7 +292,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 8,
"id": "05f598ce-88b3-42c0-add0-7d94c94c0777",
"metadata": {
"colab": {
@@ -225,7 +302,53 @@
"outputId": "1cf10905-3f65-433d-cf34-fe0cc3f26158",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-03-31 14:13:22.486989: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 1022 MB memory: -> device: 0, name: NVIDIA A40, pci bus id: 0000:41:00.0, compute capability: 8.6\n",
+ "2025-03-31 14:13:23.205860: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8907\n",
+ "2025-03-31 14:13:24.399773: I external/local_xla/xla/service/service.cc:168] XLA service 0x7fd14f3b9f40 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
+ "2025-03-31 14:13:24.399848: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA A40, Compute Capability 8.6\n",
+ "2025-03-31 14:13:24.405514: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
+ "WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
+ "I0000 00:00:1743423204.515165 2634709 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1875/1875 [==============================] - 7s 2ms/step - loss: 0.5067 - accuracy: 0.8469 - val_loss: 0.3242 - val_accuracy: 0.8837\n",
+ "Epoch 2/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2978 - accuracy: 0.8904 - val_loss: 0.3142 - val_accuracy: 0.8830\n",
+ "Epoch 3/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2635 - accuracy: 0.9027 - val_loss: 0.3030 - val_accuracy: 0.8907\n",
+ "Epoch 4/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2395 - accuracy: 0.9112 - val_loss: 0.2789 - val_accuracy: 0.9007\n",
+ "Epoch 5/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2239 - accuracy: 0.9152 - val_loss: 0.3080 - val_accuracy: 0.8894\n",
+ "Epoch 6/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2075 - accuracy: 0.9227 - val_loss: 0.2764 - val_accuracy: 0.9049\n",
+ "Epoch 7/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1954 - accuracy: 0.9273 - val_loss: 0.2995 - val_accuracy: 0.9044\n",
+ "Epoch 8/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1807 - accuracy: 0.9319 - val_loss: 0.2761 - val_accuracy: 0.9122\n",
+ "Epoch 9/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1733 - accuracy: 0.9357 - val_loss: 0.3064 - val_accuracy: 0.9086\n",
+ "Epoch 10/10\n",
+ "1875/1875 [==============================] - 5s 2ms/step - loss: 0.1611 - accuracy: 0.9407 - val_loss: 0.3120 - val_accuracy: 0.9055\n"
+ ]
+ }
+ ],
"source": [
"# Set random seed\n",
"tf.random.set_seed(42)\n",
@@ -266,7 +389,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 9,
"id": "af3c9db1-5991-4b7b-bd27-8568ed280128",
"metadata": {
"colab": {
@@ -276,7 +399,41 @@
"outputId": "6c30b8af-5f19-4c4c-c65e-68436f8d87d7",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " conv2d (Conv2D) (None, 28, 28, 32) 320 \n",
+ " \n",
+ " max_pooling2d (MaxPooling2 (None, 14, 14, 32) 0 \n",
+ " D) \n",
+ " \n",
+ " conv2d_1 (Conv2D) (None, 14, 14, 64) 18496 \n",
+ " \n",
+ " max_pooling2d_1 (MaxPoolin (None, 7, 7, 64) 0 \n",
+ " g2D) \n",
+ " \n",
+ " flatten (Flatten) (None, 3136) 0 \n",
+ " \n",
+ " dropout (Dropout) (None, 3136) 0 \n",
+ " \n",
+ " dense (Dense) (None, 128) 401536 \n",
+ " \n",
+ " dense_1 (Dense) (None, 10) 1290 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 421642 (1.61 MB)\n",
+ "Trainable params: 421642 (1.61 MB)\n",
+ "Non-trainable params: 0 (0.00 Byte)\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
"source": [
"CNN_model_1.summary()"
]
@@ -293,7 +450,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 10,
"id": "9e633182-deb2-4a9b-bb2e-86b060d527cb",
"metadata": {
"colab": {
@@ -303,7 +460,18 @@
"outputId": "334ad24e-1c94-4973-8b4c-3e3c1eef7bc7",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0, 255)"
+ ]
+ },
+ "execution_count": 10,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Check the min and max values of the training data\n",
"train_data.min(), train_data.max()"
@@ -311,7 +479,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 11,
"id": "70830f10-7796-40b7-9622-75dbe13c6524",
"metadata": {
"colab": {
@@ -321,7 +489,18 @@
"outputId": "5443c261-26ad-49f8-cf36-37d259224037",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(0.0, 1.0)"
+ ]
+ },
+ "execution_count": 11,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
"source": [
"# Divide train and test images by the maximum value (normalize it)\n",
"train_data = train_data / 255.0\n",
@@ -333,7 +512,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 12,
"id": "6d656976-f555-43d6-9e18-8cbc1005b106",
"metadata": {
"colab": {
@@ -343,7 +522,34 @@
"outputId": "340beb7f-f094-4225-b8a4-0422aab83c6c",
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n",
+ "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4132 - accuracy: 0.8513 - val_loss: 0.3214 - val_accuracy: 0.8860\n",
+ "Epoch 2/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2737 - accuracy: 0.8996 - val_loss: 0.3310 - val_accuracy: 0.8793\n",
+ "Epoch 3/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2303 - accuracy: 0.9152 - val_loss: 0.2519 - val_accuracy: 0.9059\n",
+ "Epoch 4/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2023 - accuracy: 0.9239 - val_loss: 0.2354 - val_accuracy: 0.9125\n",
+ "Epoch 5/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1772 - accuracy: 0.9335 - val_loss: 0.2416 - val_accuracy: 0.9153\n",
+ "Epoch 6/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1579 - accuracy: 0.9401 - val_loss: 0.2508 - val_accuracy: 0.9107\n",
+ "Epoch 7/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1412 - accuracy: 0.9475 - val_loss: 0.2321 - val_accuracy: 0.9213\n",
+ "Epoch 8/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1225 - accuracy: 0.9539 - val_loss: 0.2453 - val_accuracy: 0.9199\n",
+ "Epoch 9/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1124 - accuracy: 0.9572 - val_loss: 0.2410 - val_accuracy: 0.9209\n",
+ "Epoch 10/10\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.0995 - accuracy: 0.9624 - val_loss: 0.2535 - val_accuracy: 0.9216\n"
+ ]
+ }
+ ],
"source": [
"# Set random seed\n",
"tf.random.set_seed(42)\n",
@@ -395,10 +601,27 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 13,
"id": "2a95c566-87e3-4532-98b3-2a6dd9e32883",
"metadata": {},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/5\n",
+ "1875/1875 [==============================] - 5s 2ms/step - loss: 0.4855 - accuracy: 0.8231 - val_loss: 0.3559 - val_accuracy: 0.8741\n",
+ "Epoch 2/5\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3226 - accuracy: 0.8828 - val_loss: 0.3640 - val_accuracy: 0.8658\n",
+ "Epoch 3/5\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2805 - accuracy: 0.8968 - val_loss: 0.2929 - val_accuracy: 0.8924\n",
+ "Epoch 4/5\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2529 - accuracy: 0.9068 - val_loss: 0.2576 - val_accuracy: 0.9074\n",
+ "Epoch 5/5\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2316 - accuracy: 0.9152 - val_loss: 0.2592 - val_accuracy: 0.9061\n"
+ ]
+ }
+ ],
"source": [
"# Set random seed\n",
"tf.random.set_seed(42)\n",
@@ -432,24 +655,108 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 14,
"id": "9c27ac86-7b2f-4199-9f96-c2fae9609c6f",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Model: \"sequential_2\"\n",
+ "_________________________________________________________________\n",
+ " Layer (type) Output Shape Param # \n",
+ "=================================================================\n",
+ " conv2d_4 (Conv2D) (None, 28, 28, 32) 320 \n",
+ " \n",
+ " max_pooling2d_4 (MaxPoolin (None, 14, 14, 32) 0 \n",
+ " g2D) \n",
+ " \n",
+ " conv2d_5 (Conv2D) (None, 14, 14, 64) 18496 \n",
+ " \n",
+ " max_pooling2d_5 (MaxPoolin (None, 7, 7, 64) 0 \n",
+ " g2D) \n",
+ " \n",
+ " conv2d_6 (Conv2D) (None, 7, 7, 128) 32896 \n",
+ " \n",
+ " max_pooling2d_6 (MaxPoolin (None, 3, 3, 128) 0 \n",
+ " g2D) \n",
+ " \n",
+ " flatten_2 (Flatten) (None, 1152) 0 \n",
+ " \n",
+ " dropout_2 (Dropout) (None, 1152) 0 \n",
+ " \n",
+ " dense_4 (Dense) (None, 10) 11530 \n",
+ " \n",
+ "=================================================================\n",
+ "Total params: 63242 (247.04 KB)\n",
+ "Trainable params: 63242 (247.04 KB)\n",
+ "Non-trainable params: 0 (0.00 Byte)\n",
+ "_________________________________________________________________\n"
+ ]
+ }
+ ],
"source": [
"CNN_model_3.summary()"
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 15,
"id": "adae0af2-f6d3-4d7e-8d66-d313f76df16b",
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/20\n",
+ "1875/1875 [==============================] - 6s 2ms/step - loss: 0.5503 - accuracy: 0.7978 - val_loss: 0.3697 - val_accuracy: 0.8693\n",
+ "Epoch 2/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3692 - accuracy: 0.8652 - val_loss: 0.3445 - val_accuracy: 0.8757\n",
+ "Epoch 3/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.3216 - accuracy: 0.8817 - val_loss: 0.2909 - val_accuracy: 0.8919\n",
+ "Epoch 4/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2951 - accuracy: 0.8916 - val_loss: 0.2648 - val_accuracy: 0.9034\n",
+ "Epoch 5/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2767 - accuracy: 0.8982 - val_loss: 0.2612 - val_accuracy: 0.9048\n",
+ "Epoch 6/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2623 - accuracy: 0.9028 - val_loss: 0.2510 - val_accuracy: 0.9098\n",
+ "Epoch 7/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2472 - accuracy: 0.9072 - val_loss: 0.2383 - val_accuracy: 0.9106\n",
+ "Epoch 8/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2395 - accuracy: 0.9107 - val_loss: 0.2457 - val_accuracy: 0.9086\n",
+ "Epoch 9/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2331 - accuracy: 0.9129 - val_loss: 0.2311 - val_accuracy: 0.9144\n",
+ "Epoch 10/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2247 - accuracy: 0.9161 - val_loss: 0.2371 - val_accuracy: 0.9125\n",
+ "Epoch 11/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2194 - accuracy: 0.9184 - val_loss: 0.2229 - val_accuracy: 0.9193\n",
+ "Epoch 12/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2100 - accuracy: 0.9212 - val_loss: 0.2251 - val_accuracy: 0.9169\n",
+ "Epoch 13/20\n",
+ "1875/1875 [==============================] - 6s 3ms/step - loss: 0.2067 - accuracy: 0.9220 - val_loss: 0.2159 - val_accuracy: 0.9210\n",
+ "Epoch 14/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.2021 - accuracy: 0.9243 - val_loss: 0.2236 - val_accuracy: 0.9183\n",
+ "Epoch 15/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1983 - accuracy: 0.9255 - val_loss: 0.2116 - val_accuracy: 0.9234\n",
+ "Epoch 16/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1935 - accuracy: 0.9265 - val_loss: 0.2124 - val_accuracy: 0.9232\n",
+ "Epoch 17/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1881 - accuracy: 0.9291 - val_loss: 0.2129 - val_accuracy: 0.9234\n",
+ "Epoch 18/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1853 - accuracy: 0.9292 - val_loss: 0.2085 - val_accuracy: 0.9255\n",
+ "Epoch 19/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1817 - accuracy: 0.9318 - val_loss: 0.2196 - val_accuracy: 0.9200\n",
+ "Epoch 20/20\n",
+ "1875/1875 [==============================] - 4s 2ms/step - loss: 0.1787 - accuracy: 0.9336 - val_loss: 0.2132 - val_accuracy: 0.9229\n"
+ ]
+ }
+ ],
"source": [
"# Set random seed\n",
"tf.random.set_seed(42)\n",
@@ -507,7 +814,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/14_Transfer_learning.ipynb b/14_Transfer_learning.ipynb
index c69ecf8710f858090d255f1d1915552c2402bec6..0b49909890118a2cef5303d7bbd742fca197ffd3 100644
--- a/14_Transfer_learning.ipynb
+++ b/14_Transfer_learning.ipynb
@@ -1266,7 +1266,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/15_RNNs.ipynb b/15_RNNs.ipynb
index a0ba3f4c52d07762440ab849950c32653db4304e..33d07d33067103db210e9804257b808b6e77f0d9 100644
--- a/15_RNNs.ipynb
+++ b/15_RNNs.ipynb
@@ -42,7 +42,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 1,
"metadata": {
"colab_type": "code",
"tags": []
@@ -71,7 +71,7 @@
},
{
"cell_type": "code",
- "execution_count": 1,
+ "execution_count": 2,
"metadata": {
"colab_type": "code",
"tags": []
@@ -113,7 +113,7 @@
},
{
"cell_type": "code",
- "execution_count": 2,
+ "execution_count": 3,
"metadata": {
"colab_type": "code",
"tags": []
@@ -125,7 +125,7 @@
},
{
"cell_type": "code",
- "execution_count": 3,
+ "execution_count": 4,
"metadata": {
"colab_type": "code",
"tags": []
@@ -138,7 +138,7 @@
},
{
"cell_type": "code",
- "execution_count": 4,
+ "execution_count": 5,
"metadata": {
"colab_type": "code",
"tags": []
@@ -164,7 +164,7 @@
},
{
"cell_type": "code",
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {
"colab_type": "code",
"tags": []
@@ -173,10 +173,10 @@
{
"data": {
"text/plain": [
- "[<matplotlib.lines.Line2D at 0x7ff6ce0b2140>]"
+ "[<matplotlib.lines.Line2D at 0x7f864912f730>]"
]
},
- "execution_count": 5,
+ "execution_count": 6,
"metadata": {},
"output_type": "execute_result"
},
@@ -211,7 +211,7 @@
},
{
"cell_type": "code",
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {
"colab_type": "code"
},
@@ -219,10 +219,10 @@
{
"data": {
"text/plain": [
- "[<matplotlib.lines.Line2D at 0x7ff6cd381690>]"
+ "[<matplotlib.lines.Line2D at 0x7f8648ffec20>]"
]
},
- "execution_count": 6,
+ "execution_count": 7,
"metadata": {},
"output_type": "execute_result"
},
@@ -255,7 +255,7 @@
},
{
"cell_type": "code",
- "execution_count": 7,
+ "execution_count": 8,
"metadata": {
"colab_type": "code"
},
@@ -301,7 +301,7 @@
},
{
"cell_type": "code",
- "execution_count": 8,
+ "execution_count": 9,
"metadata": {
"colab_type": "code"
},
@@ -331,10 +331,10 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2024-10-22 15:10:54.581701: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
- "2024-10-22 15:10:54.581773: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
- "2024-10-22 15:10:54.582433: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
- "2024-10-22 15:10:54.586743: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
+ "2025-03-31 14:25:50.479492: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
+ "2025-03-31 14:25:50.480205: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
+ "2025-03-31 14:25:50.480955: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n",
+ "2025-03-31 14:25:50.487640: I tensorflow/core/platform/cpu_feature_guard.cc:182] This TensorFlow binary is optimized to use available CPU instructions in performance-critical operations.\n",
"To enable the following instructions: SSE4.1 SSE4.2 AVX AVX2 FMA, in other operations, rebuild TensorFlow with the appropriate compiler flags.\n"
]
}
@@ -366,7 +366,7 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2024-10-22 15:10:56.581280: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 856 MB memory: -> device: 0, name: NVIDIA A100-PCIE-40GB, pci bus id: 0000:81:00.0, compute capability: 8.0\n"
+ "2025-03-31 14:25:54.260168: I tensorflow/core/common_runtime/gpu/gpu_device.cc:1929] Created device /job:localhost/replica:0/task:0/device:GPU:0 with 43604 MB memory: -> device: 0, name: NVIDIA A40, pci bus id: 0000:41:00.0, compute capability: 8.6\n"
]
}
],
@@ -554,7 +554,7 @@
},
{
"cell_type": "code",
- "execution_count": 16,
+ "execution_count": 15,
"metadata": {
"colab_type": "code"
},
@@ -566,7 +566,7 @@
},
{
"cell_type": "code",
- "execution_count": 17,
+ "execution_count": 16,
"metadata": {
"colab_type": "code"
},
@@ -582,19 +582,19 @@
"name": "stderr",
"output_type": "stream",
"text": [
- "2024-10-22 15:19:05.085993: I external/local_xla/xla/service/service.cc:168] XLA service 0x7ff14e2cff80 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
- "2024-10-22 15:19:05.086052: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA A100-PCIE-40GB, Compute Capability 8.0\n",
- "2024-10-22 15:19:05.090628: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
- "2024-10-22 15:19:05.114273: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8907\n",
+ "2025-03-31 14:26:01.988615: I external/local_xla/xla/service/service.cc:168] XLA service 0x7f84c9242220 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:\n",
+ "2025-03-31 14:26:01.988675: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): NVIDIA A40, Compute Capability 8.6\n",
+ "2025-03-31 14:26:01.994162: I tensorflow/compiler/mlir/tensorflow/utils/dump_mlir_util.cc:269] disabling MLIR crash reproducer, set env var `MLIR_CRASH_REPRODUCER_DIRECTORY` to enable.\n",
+ "2025-03-31 14:26:02.011307: I external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:454] Loaded cuDNN version 8907\n",
"WARNING: All log messages before absl::InitializeLog() is called are written to STDERR\n",
- "I0000 00:00:1729603145.179008 529342 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
+ "I0000 00:00:1743423962.075935 2640387 device_compiler.h:186] Compiled cluster using XLA! This line is logged at most once for the lifetime of the process.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
- "805/819 [============================>.] - ETA: 0s - loss: 13.6027 - mae: 2.8355INFO:tensorflow:Assets written to: jena_dense.tf/assets\n"
+ "808/819 [============================>.] - ETA: 0s - loss: 11.9416 - mae: 2.6733INFO:tensorflow:Assets written to: jena_dense.tf/assets\n"
]
},
{
@@ -608,26 +608,26 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "819/819 [==============================] - 7s 8ms/step - loss: 13.5365 - mae: 2.8286 - val_loss: 10.1460 - val_mae: 2.5093\n",
+ "819/819 [==============================] - 10s 10ms/step - loss: 11.9150 - mae: 2.6708 - val_loss: 10.4202 - val_mae: 2.5304\n",
"Epoch 2/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 9.1866 - mae: 2.3810 - val_loss: 10.9637 - val_mae: 2.6022\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 8.7603 - mae: 2.3292 - val_loss: 14.1843 - val_mae: 2.9976\n",
"Epoch 3/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 8.4958 - mae: 2.2943 - val_loss: 10.6133 - val_mae: 2.5832\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 8.1068 - mae: 2.2383 - val_loss: 11.1684 - val_mae: 2.6239\n",
"Epoch 4/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 8.0837 - mae: 2.2365 - val_loss: 10.3756 - val_mae: 2.5515\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 7.6811 - mae: 2.1793 - val_loss: 10.7352 - val_mae: 2.5778\n",
"Epoch 5/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 7.7701 - mae: 2.1965 - val_loss: 10.7008 - val_mae: 2.5841\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 7.3762 - mae: 2.1367 - val_loss: 10.4626 - val_mae: 2.5311\n",
"Epoch 6/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 7.5241 - mae: 2.1619 - val_loss: 10.9311 - val_mae: 2.6145\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 7.1359 - mae: 2.1026 - val_loss: 10.6801 - val_mae: 2.5636\n",
"Epoch 7/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 7.3582 - mae: 2.1386 - val_loss: 10.7701 - val_mae: 2.5990\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 6.9628 - mae: 2.0772 - val_loss: 10.7338 - val_mae: 2.5696\n",
"Epoch 8/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 7.1554 - mae: 2.1088 - val_loss: 11.1616 - val_mae: 2.6537\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 6.8303 - mae: 2.0567 - val_loss: 11.4170 - val_mae: 2.6528\n",
"Epoch 9/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 7.0308 - mae: 2.0919 - val_loss: 11.1549 - val_mae: 2.6442\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 6.7077 - mae: 2.0368 - val_loss: 11.0706 - val_mae: 2.6094\n",
"Epoch 10/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 6.8780 - mae: 2.0686 - val_loss: 10.9421 - val_mae: 2.6263\n",
- "405/405 [==============================] - 2s 5ms/step - loss: 11.2737 - mae: 2.6261\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 6.5925 - mae: 2.0218 - val_loss: 10.9389 - val_mae: 2.6011\n",
+ "405/405 [==============================] - 3s 6ms/step - loss: 11.2481 - mae: 2.6301\n",
"Test MAE: 2.63\n"
]
}
@@ -671,14 +671,14 @@
},
{
"cell_type": "code",
- "execution_count": 18,
+ "execution_count": 17,
"metadata": {
"colab_type": "code"
},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -720,7 +720,7 @@
},
{
"cell_type": "code",
- "execution_count": 19,
+ "execution_count": 18,
"metadata": {
"colab_type": "code"
},
@@ -730,7 +730,7 @@
"output_type": "stream",
"text": [
"Epoch 1/10\n",
- "819/819 [==============================] - ETA: 0s - loss: 20.4148 - mae: 3.5417INFO:tensorflow:Assets written to: jena_conv.tf/assets\n"
+ "819/819 [==============================] - ETA: 0s - loss: 22.1074 - mae: 3.6817INFO:tensorflow:Assets written to: jena_conv.tf/assets\n"
]
},
{
@@ -744,9 +744,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "819/819 [==============================] - 8s 8ms/step - loss: 20.4148 - mae: 3.5417 - val_loss: 17.3305 - val_mae: 3.3039\n",
+ "819/819 [==============================] - 11s 11ms/step - loss: 22.1074 - mae: 3.6817 - val_loss: 17.2070 - val_mae: 3.2492\n",
"Epoch 2/10\n",
- "806/819 [============================>.] - ETA: 0s - loss: 14.4797 - mae: 3.0240INFO:tensorflow:Assets written to: jena_conv.tf/assets\n"
+ "808/819 [============================>.] - ETA: 0s - loss: 15.8823 - mae: 3.1701INFO:tensorflow:Assets written to: jena_conv.tf/assets\n"
]
},
{
@@ -760,49 +760,17 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "819/819 [==============================] - 6s 8ms/step - loss: 14.4621 - mae: 3.0220 - val_loss: 14.9600 - val_mae: 3.0368\n",
+ "819/819 [==============================] - 9s 10ms/step - loss: 15.8742 - mae: 3.1699 - val_loss: 14.9139 - val_mae: 3.0457\n",
"Epoch 3/10\n",
- "819/819 [==============================] - ETA: 0s - loss: 13.1402 - mae: 2.8770INFO:tensorflow:Assets written to: jena_conv.tf/assets\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:tensorflow:Assets written to: jena_conv.tf/assets\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "819/819 [==============================] - 6s 8ms/step - loss: 13.1402 - mae: 2.8770 - val_loss: 14.3608 - val_mae: 2.9843\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 14.5451 - mae: 3.0339 - val_loss: 15.3480 - val_mae: 3.0655\n",
"Epoch 4/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 12.2463 - mae: 2.7731 - val_loss: 15.1598 - val_mae: 3.0882\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 13.6292 - mae: 2.9313 - val_loss: 16.5140 - val_mae: 3.2385\n",
"Epoch 5/10\n",
- "808/819 [============================>.] - ETA: 0s - loss: 11.6198 - mae: 2.7028INFO:tensorflow:Assets written to: jena_conv.tf/assets\n"
- ]
- },
- {
- "name": "stderr",
- "output_type": "stream",
- "text": [
- "INFO:tensorflow:Assets written to: jena_conv.tf/assets\n"
- ]
- },
- {
- "name": "stdout",
- "output_type": "stream",
- "text": [
- "819/819 [==============================] - 6s 8ms/step - loss: 11.6175 - mae: 2.7027 - val_loss: 13.5547 - val_mae: 2.9108\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 12.9731 - mae: 2.8545 - val_loss: 15.3477 - val_mae: 3.0685\n",
"Epoch 6/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 11.0951 - mae: 2.6415 - val_loss: 14.4117 - val_mae: 2.9762\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 12.4869 - mae: 2.7957 - val_loss: 15.4494 - val_mae: 3.1214\n",
"Epoch 7/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 10.6621 - mae: 2.5885 - val_loss: 14.6331 - val_mae: 2.9957\n",
- "Epoch 8/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 10.3075 - mae: 2.5449 - val_loss: 14.0174 - val_mae: 2.9436\n",
- "Epoch 9/10\n",
- "813/819 [============================>.] - ETA: 0s - loss: 10.0300 - mae: 2.5072INFO:tensorflow:Assets written to: jena_conv.tf/assets\n"
+ "809/819 [============================>.] - ETA: 0s - loss: 12.0365 - mae: 2.7409INFO:tensorflow:Assets written to: jena_conv.tf/assets\n"
]
},
{
@@ -816,11 +784,15 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "819/819 [==============================] - 6s 7ms/step - loss: 10.0312 - mae: 2.5075 - val_loss: 13.4672 - val_mae: 2.8916\n",
+ "819/819 [==============================] - 9s 10ms/step - loss: 12.0380 - mae: 2.7413 - val_loss: 14.2460 - val_mae: 2.9775\n",
+ "Epoch 8/10\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 11.6246 - mae: 2.6928 - val_loss: 15.3648 - val_mae: 3.1282\n",
+ "Epoch 9/10\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 11.2826 - mae: 2.6554 - val_loss: 15.1919 - val_mae: 3.0577\n",
"Epoch 10/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 9.8150 - mae: 2.4815 - val_loss: 13.6660 - val_mae: 2.9005\n",
- "405/405 [==============================] - 2s 5ms/step - loss: 14.9401 - mae: 3.0604\n",
- "Test MAE: 3.06\n"
+ "819/819 [==============================] - 8s 10ms/step - loss: 10.9090 - mae: 2.6114 - val_loss: 14.9201 - val_mae: 3.0366\n",
+ "405/405 [==============================] - 3s 7ms/step - loss: 15.0884 - mae: 3.0774\n",
+ "Test MAE: 3.08\n"
]
}
],
@@ -851,12 +823,12 @@
},
{
"cell_type": "code",
- "execution_count": 20,
+ "execution_count": 19,
"metadata": {},
"outputs": [
{
"data": {
- "image/png": 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",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -909,7 +881,7 @@
},
{
"cell_type": "code",
- "execution_count": 21,
+ "execution_count": 20,
"metadata": {
"colab_type": "code"
},
@@ -919,7 +891,7 @@
"output_type": "stream",
"text": [
"Epoch 1/10\n",
- "811/819 [============================>.] - ETA: 0s - loss: 37.6936 - mae: 4.4764INFO:tensorflow:Assets written to: jena_lstm.tf/assets\n"
+ "810/819 [============================>.] - ETA: 0s - loss: 35.3958 - mae: 4.3118INFO:tensorflow:Assets written to: jena_lstm.tf/assets\n"
]
},
{
@@ -933,9 +905,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "819/819 [==============================] - 9s 9ms/step - loss: 37.4740 - mae: 4.4613 - val_loss: 12.0897 - val_mae: 2.6533\n",
+ "819/819 [==============================] - 12s 13ms/step - loss: 35.1575 - mae: 4.2951 - val_loss: 11.9484 - val_mae: 2.6663\n",
"Epoch 2/10\n",
- "814/819 [============================>.] - ETA: 0s - loss: 10.9294 - mae: 2.5632INFO:tensorflow:Assets written to: jena_lstm.tf/assets\n"
+ "813/819 [============================>.] - ETA: 0s - loss: 10.6597 - mae: 2.5403INFO:tensorflow:Assets written to: jena_lstm.tf/assets\n"
]
},
{
@@ -949,13 +921,9 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "819/819 [==============================] - 7s 9ms/step - loss: 10.9207 - mae: 2.5622 - val_loss: 9.5662 - val_mae: 2.4069\n",
+ "819/819 [==============================] - 10s 12ms/step - loss: 10.6566 - mae: 2.5400 - val_loss: 9.7913 - val_mae: 2.4270\n",
"Epoch 3/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 9.7121 - mae: 2.4224 - val_loss: 9.6075 - val_mae: 2.4214\n",
- "Epoch 4/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 9.2282 - mae: 2.3580 - val_loss: 9.6919 - val_mae: 2.4339\n",
- "Epoch 5/10\n",
- "819/819 [==============================] - ETA: 0s - loss: 8.8512 - mae: 2.3116INFO:tensorflow:Assets written to: jena_lstm.tf/assets\n"
+ "815/819 [============================>.] - ETA: 0s - loss: 9.7244 - mae: 2.4323INFO:tensorflow:Assets written to: jena_lstm.tf/assets\n"
]
},
{
@@ -969,19 +937,23 @@
"name": "stdout",
"output_type": "stream",
"text": [
- "819/819 [==============================] - 7s 9ms/step - loss: 8.8512 - mae: 2.3116 - val_loss: 9.4065 - val_mae: 2.3974\n",
+ "819/819 [==============================] - 10s 12ms/step - loss: 9.7233 - mae: 2.4320 - val_loss: 9.5637 - val_mae: 2.4022\n",
+ "Epoch 4/10\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 9.3103 - mae: 2.3753 - val_loss: 9.6566 - val_mae: 2.4170\n",
+ "Epoch 5/10\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 8.9971 - mae: 2.3325 - val_loss: 9.9187 - val_mae: 2.4391\n",
"Epoch 6/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 8.5026 - mae: 2.2696 - val_loss: 9.6882 - val_mae: 2.4322\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 8.7352 - mae: 2.2953 - val_loss: 9.9262 - val_mae: 2.4381\n",
"Epoch 7/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 8.2932 - mae: 2.2416 - val_loss: 9.8238 - val_mae: 2.4506\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 8.5969 - mae: 2.2745 - val_loss: 9.9293 - val_mae: 2.4352\n",
"Epoch 8/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 8.1197 - mae: 2.2184 - val_loss: 9.6106 - val_mae: 2.4139\n",
+ "819/819 [==============================] - 9s 11ms/step - loss: 8.4870 - mae: 2.2566 - val_loss: 10.6567 - val_mae: 2.4853\n",
"Epoch 9/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 8.0228 - mae: 2.2023 - val_loss: 9.9090 - val_mae: 2.4549\n",
+ "819/819 [==============================] - 8s 10ms/step - loss: 8.3403 - mae: 2.2361 - val_loss: 10.6111 - val_mae: 2.4920\n",
"Epoch 10/10\n",
- "819/819 [==============================] - 6s 7ms/step - loss: 7.8854 - mae: 2.1835 - val_loss: 10.1699 - val_mae: 2.4799\n",
- "405/405 [==============================] - 2s 4ms/step - loss: 10.7116 - mae: 2.5950\n",
- "Test MAE: 2.60\n"
+ "819/819 [==============================] - 8s 10ms/step - loss: 8.2129 - mae: 2.2192 - val_loss: 10.3386 - val_mae: 2.4991\n",
+ "405/405 [==============================] - 3s 7ms/step - loss: 10.8591 - mae: 2.5876\n",
+ "Test MAE: 2.59\n"
]
}
],
@@ -1014,14 +986,14 @@
},
{
"cell_type": "code",
- "execution_count": 22,
+ "execution_count": 21,
"metadata": {
"tags": []
},
"outputs": [
{
"data": {
- "image/png": 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//LBiY2Pl4+OjWbNm2dXz8vLS8uXLVb9+fQ0fPlz9+/fX0aNH9e233zocs2PHjpo4caKWL1+utm3b6qabbtK2bducnt/Hx0cpKSkaOHCgXnvtNd1xxx1KTEzUuHHjlJycXKLXVFqOHz+u5cuXq1evXk6DiCTdf//98vX1VXx8vF354MGD1bp1a4ft888/vxZNhxuyGJf2uQIo06ZNm6ZnnnlG+/fvL/HKsABQ1jBMA5RR+X+9N2jQQBcvXtSaNWv09ttvKzY2liACwK0QRoAyys/PT2+++ab27t2rrKws1apVSxMmTNAzzzxjdtMAoFQxTAMAAEzFBFYAAGAqwggAADAVYQQAAJiqXExgzcvL0+HDhxUYGFjgzZ4AAEDZYhiGTp8+rYiIiEIXaywXYeTw4cMF3lsBAACUbQcOHCh0SYJyEUbyl1k+cOCAgoKCTG4NAABwRWZmpqKiopzeLuFS5SKM5A/NBAUFEUYAAChnippiwQRWAABgKsIIAAAwFWEEAACYqlzMGQEAlJxhGMrJyVFubq7ZTYGb8fDwkKen5xUvu3FFYWT69OmaNGmSxowZo5kzZzqtk5qaqk6dOjmU79q1Sw0aNLiS0wMAipCdna2MjAydO3fO7KbATfn5+Sk8PFyVK1cu8TFKHEa2bt2quXPnqlmzZi7V37Nnj92VMNddd11JTw0AcEFeXp7S09Pl4eGhiIgIVa5cmYUjUWoMw1B2draOHTum9PR0XX/99YUubFaYEoWRM2fOaODAgXr//fc1ZcoUl/YJCQlRlSpVSnI6AEAJZGdnKy8vT1FRUfLz8zO7OXBDvr6+8vLy0r59+5SdnS0fH58SHadEEWbkyJHq2bOnbr/9dpf3ufHGGxUeHq7OnTsrJSWl0LpZWVnKzMy02wAAJVPSv1YBV5TGz1exe0aWLFmiH374QVu3bnWpfnh4uObOnauWLVsqKytLCxYsUOfOnZWamqr27ds73Wf69Ol64YUXitu0YsnNldavlzIypPBwqV07ycPjqp4SAAA4UawwcuDAAY0ZM0YrV650uSsmJiZGMTExtsetW7fWgQMH9PrrrxcYRiZOnKjHH3/c9jh/OdnSkpwsjRkjHTz4v7LISOmtt6R+/UrtNAAAwAXF6lvZtm2bjh49qpYtW8rT01Oenp5au3at3n77bXl6erp82dgtt9yin3/+ucDnvb29bUu/l/YS8MnJ0j332AcRSTp0yFqenFxqpwIAt5GbK6WmSosXW7+Wx6uEO3bsqLFjx7pcf+/evbJYLEpLS7tqbYJVscJI586dtXPnTqWlpdm2Vq1aaeDAgUpLS5OHi+Mc27dvV3h4eIkafCVyc609Iobh+Fx+2dix5fM/GQBcLcnJUnS01KmTNGCA9Wt09NX7481isRS6DRo0qETHTU5O1ksvveRy/aioKGVkZKhJkyYlOp+r8kOPp6enDh06ZPdcRkaGbR2PvXv3OuzbtWtXeXh4aPPmzQ7PDRo0yOn3r3v37lfrpZRYsYZpAgMDHd4Uf39/Va9e3VY+ceJEHTp0SPPnz5ckzZw5U9HR0WrcuLGys7O1cOFCJSUlKSkpqZReguvWr3fsEbmUYUgHDljrdex4zZoFAGVWfm/y5X/E5fcmf/pp6Q9vZ2Rk2P69dOlSPffcc9qzZ4+tzNfX167+xYsX5eXlVeRxq1WrVqx2eHh4KCwsrFj7XImIiAjNnz9fEydOtJV9+OGHqlmzpvbv3+9Qf//+/dq0aZNGjRql+Ph43XLLLQ51unfvrnnz5tmVeXt7l37jr1CpT7HOyMiw+6ZlZ2dr3Lhxatasmdq1a6cNGzZoxYoV6mfC5IxLfr5LpR4AuDOzepPDwsJsW3BwsCwWi+3xhQsXVKVKFX388cfq2LGjfHx8tHDhQv3xxx/q37+/IiMj5efnp6ZNm2rx4sV2x718mCY6OlrTpk3TkCFDFBgYqFq1amnu3Lm25y8fpklNTZXFYtHq1avVqlUr+fn5qU2bNnZBSZKmTJmikJAQBQYGatiwYXrqqafUvHnzIl93XFycQ3BITExUXFyc0/rz5s1Tr1699PDDD2vp0qU6e/asQx1vb2+772dYWJiqVq1aZFuutSsOI6mpqXarryYmJio1NdX2ePz48frll190/vx5nThxQuvXr1ePHj2u9LQl4urIkAkjSABQ5hSnN/lamzBhgh599FHt2rVL3bp104ULF9SyZUv985//1I8//qiHHnpI999/v7777rtCjzNjxgy1atVK27dv1yOPPKKHH35Yu3fvLnSfp59+WjNmzND3338vT09PDRkyxPbcRx99pKlTp+qVV17Rtm3bVKtWLc2ePdul13TnnXfq5MmT2rBhgyRpw4YNOnHihHr37u1Q1zAMzZs3T7GxsWrQoIHq16+vjz/+2KXzlEUV6uLzdu2sV80UtAChxSJFRVnrAUBFV5Z7k8eOHat+/fqpTp06ioiIUM2aNTVu3Dg1b95cdevW1ejRo9WtWzd98sknhR6nR48eeuSRR1SvXj1NmDBBNWrUsPuD2pmpU6eqQ4cOatSokZ566ilt3LhRFy5ckCT94x//0NChQzV48GDVr19fzz33nJo2berSa/Ly8lJsbKwSEhIkSQkJCYqNjXU6BPXtt9/q3Llz6tatmyQpNjZW8fHxDvX++c9/KiAgwG4rzryZa6VChREPD+vlu5JjIMl/PHMm640AgFS2e5NbtWpl9zg3N1dTp05Vs2bNVL16dQUEBGjlypVO51pc6tJbmuQPBx09etTlffIvxsjfZ8+ePbr55pvt6l/+uDBDhw7VJ598oiNHjuiTTz6x63W5VHx8vO677z55elqnfvbv31/fffedw5BRp06d7C46SUtL08iRI11uz7VSocKIZJ1o9emnUs2a9uWRkVdnIhYAlFdluTfZ39/f7vGMGTP05ptvavz48VqzZo3S0tLUrVs3ZWdnF3qcy3sdLBaL8vLyXN4n/14/l+5z+f1/DGeTbgrQpEkTNWjQQP3791fDhg2dXslz4sQJffbZZ3r33Xdty2zUrFlTOTk5tl6VfP7+/qpXr57dVtyJvNdChQsjkjVw7N0rpaRIixZZv6anE0QA4FLlqTd5/fr16tOnj2JjY3XDDTeobt26ha5ndbXExMRoy5YtdmXff/99sY4xZMgQpaamFtgr8tFHHykyMlI7duyw6/GYOXOmPvzwQ+Xk5JS4/WYp8V17yzsPDy7fBYCi5PcmO1u1eubMsvNHXL169ZSUlKSNGzeqatWqeuONN3TkyBE1bNjwmrZj9OjRevDBB9WqVSu1adNGS5cu1b/+9S/VrVvX5WM8+OCDuvfeewu8uWx8fLzuueceh16T2rVra8KECVqxYoX69OkjyXqvtyNHjtjV8/T0VI0aNYr3wq6yChtGAACu6ddP6tOnbN/P69lnn1V6erq6desmPz8/PfTQQ+rbt69OnTp1TdsxcOBA/fbbbxo3bpwuXLigv/3tbxo0aJBDb0lhCgsL27Zt044dO/T+++87PBcYGKiuXbsqPj7eFka+/vprh0VGY2Jiirxi6FqzGMUZzDJJZmamgoODderUqVJdGh4A3NmFCxeUnp6uOnXqlPjW7rhyXbp0UVhYmBYsWGB2U66Kwn7OXP38pmcEAIBScu7cOc2ZM0fdunWTh4eHFi9erG+//VarVq0yu2llGmEEAIBSYrFY9OWXX2rKlCnKyspSTEyMkpKSdPvtt5vdtDKNMAIAQCnx9fXVt99+a3Yzyp0KeWkvAAAoOwgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBALiljh07auzYsbbH0dHRmjlzZqH7WCwWffbZZ1d87tI6TkVBGAEAlCm9e/cucJGwTZs2yWKx6Icffij2cbdu3aqHHnroSptnZ/LkyWrevLlDeUZGhu64445SPdflEhMTZbFYnN4M8OOPP5bFYlF0dLTDc+fPn1fVqlVVrVo1nT9/3uH56OhoWSwWh+3ll1++Gi9DEmEEAFDGDB06VGvWrNG+ffscnktISFDz5s3VokWLYh/3uuuuk5+fX2k0sUhhYWHy9va+6ufx9/fX0aNHtWnTJrvyhIQE1apVy+k+SUlJatKkiRo1aqTk5GSndV588UVlZGTYbaNHjy719ucjjABABWIY0tmz5myu3pa1V69eCgkJUWJiol35uXPntHTpUg0dOlR//PGH+vfvr8jISPn5+alp06ZavHhxoce9fJjm559/Vvv27eXj46NGjRo5vX/MhAkTVL9+ffn5+alu3bp69tlndfHiRUnWnokXXnhBO3bssPUe5Lf58mGanTt36rbbbpOvr6+qV6+uhx56SGfOnLE9P2jQIPXt21evv/66wsPDVb16dY0cOdJ2roJ4enpqwIABSkhIsJUdPHhQqampGjBggNN94uPjFRsbq9jYWMXHxzutExgYqLCwMLvN39+/0LZcCZaDB4AK5Nw5KSDAnHOfOSO58nnm6empBx54QImJiXruuedksVgkSZ988omys7M1cOBAnTt3Ti1bttSECRMUFBSkFStW6P7771fdunX117/+tchz5OXlqV+/fqpRo4Y2b96szMxMu/kl+QIDA5WYmKiIiAjt3LlTDz74oAIDAzV+/Hjdd999+vHHH/X111/bloAPDg52OMa5c+fUvXt33XLLLdq6dauOHj2qYcOGadSoUXaBKyUlReHh4UpJSdEvv/yi++67T82bN9eDDz5Y6GsZOnSo2rdvr7feekt+fn5KTExU9+7dFRoa6lD3119/1aZNm5ScnCzDMDR27Fj99ttvqlu3bpHfs6uJnhEAQJkzZMgQ7d27V6mpqbayhIQE9evXT1WrVlXNmjU1btw4NW/eXHXr1tXo0aPVrVs3ffLJJy4d/9tvv9WuXbu0YMECNW/eXO3bt9e0adMc6j3zzDNq06aNoqOj1bt3bz3xxBP6+OOPJVnvQxMQECBPT09b74Gvr6/DMT766COdP39e8+fPV5MmTXTbbbdp1qxZWrBggX7//XdbvapVq2rWrFlq0KCBevXqpZ49e2r16tVFvpbmzZvrL3/5iz799FMZhqHExEQNGTLEad2EhATdcccdtjkj3bt3t+tVyTdhwgQFBATYbZe+F6WNnhEAqED8/Kw9FGad21UNGjRQmzZtlJCQoE6dOunXX3/V+vXrtXLlSklSbm6uXn75ZS1dulSHDh1SVlaWsrKyXB5K2LVrl2rVqqXIyEhbWevWrR3qffrpp5o5c6Z++eUXnTlzRjk5OQoKCnL9hfz3XDfccINd22699Vbl5eVpz549th6Mxo0by8PDw1YnPDxcO3fudOkcQ4YM0bx581SrVi2dOXNGPXr00KxZs+zq5Obm6sMPP9Rbb71lK4uNjdVjjz2mF154we7cTz75pAYNGmS3f82aNV1+zcVFGAGACsRicW2opCwYOnSoRo0apXfeeUfz5s1T7dq11blzZ0nSjBkz9Oabb2rmzJlq2rSp/P39NXbsWGVnZ7t0bMPJBJb84aB8mzdv1t///ne98MIL6tatm4KDg7VkyRLNmDGjWK/DMAyHYzs7p5eXl8NzeXl5Lp1j4MCBGj9+vCZPnqwHHnhAnp6OH+/ffPONDh06pPvuu8+uPDc3VytXrrS7+qdGjRqqV6+eS+cuDQzTAADKpL/97W/y8PDQokWL9OGHH2rw4MG2D+/169erT58+io2N1Q033KC6devq559/dvnYjRo10v79+3X48GFb2eVXpPzf//2fateuraefflqtWrXS9ddf73CFT+XKlZWbm1vkudLS0nT27Fm7Y1eqVEn169d3uc2FqVatmu68806tXbu2wCGa+Ph4/f3vf1daWprdNnDgwAInsl4rhBEAQJkUEBCg++67T5MmTdLhw4fthg3q1aunVatWaePGjdq1a5eGDx+uI0eOuHzs22+/XTExMXrggQe0Y8cOrV+/Xk8//bRdnXr16mn//v1asmSJfv31V7399ttatmyZXZ3o6Gilp6crLS1Nx48fV1ZWlsO5Bg4cKB8fH8XFxenHH39USkqKRo8erfvvv9/pJNOSSkxM1PHjx9WgQQOH544dO6bly5crLi5OTZo0sdvi4uL0xRdf6NixY7b6p0+f1pEjR+y2zMzMUmvr5QgjAIAya+jQoTp58qRuv/12u3Uznn32WbVo0ULdunVTx44dFRYWpr59+7p83EqVKmnZsmXKysrSzTffrGHDhmnq1Kl2dfr06aPHHntMo0aNUvPmzbVx40Y9++yzdnXuvvtude/eXZ06ddJ1113n9PJiPz8/ffPNNzpx4oRuuukm3XPPPercubPDnI4rlX/ZsDPz58+Xv7+/bZjrUp06dVJgYKAWLFhgK3vuuecUHh5ut40fP75U23spi+Fs4KyMyczMVHBwsE6dOlXsiUMAUFFduHBB6enpqlOnjnx8fMxuDtxUYT9nrn5+0zMCAABMRRgBAACmIowAAABTEUYAAICpCCMA4ObKwXUKKMdK4+eLMAIAbip/Rc9z586Z3BK4s/yfr8tXkC0OloMHADfl4eGhKlWq6OjRo5Ks610UtCw5UFyGYejcuXM6evSoqlSpYndvm+IijACAGwsLC5MkWyABSluVKlVsP2clRRgBADdmsVgUHh6ukJAQXbx40ezmwM14eXldUY9IPsIIAFQAHh4epfKhAVwNTGAFAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmuqIwMn36dFksFo0dO7bQemvXrlXLli3l4+OjunXras6cOVdyWgAA4EZKHEa2bt2quXPnqlmzZoXWS09PV48ePdSuXTtt375dkyZN0qOPPqqkpKSSnhoAALiREoWRM2fOaODAgXr//fdVtWrVQuvOmTNHtWrV0syZM9WwYUMNGzZMQ4YM0euvv16iBgMAAPdSojAycuRI9ezZU7fffnuRdTdt2qSuXbvalXXr1k3ff/+9Ll686HSfrKwsZWZm2m0AAMA9FTuMLFmyRD/88IOmT5/uUv0jR44oNDTUriw0NFQ5OTk6fvy4032mT5+u4OBg2xYVFVXcZgIAgHKiWGHkwIEDGjNmjBYuXCgfHx+X97NYLHaPDcNwWp5v4sSJOnXqlG07cOBAcZoJAADKEc/iVN62bZuOHj2qli1b2spyc3O1bt06zZo1S1lZWfLw8LDbJywsTEeOHLErO3r0qDw9PVW9enWn5/H29pa3t3dxmgYAAMqpYoWRzp07a+fOnXZlgwcPVoMGDTRhwgSHICJJrVu31vLly+3KVq5cqVatWsnLy6sETQYAAO6kWGEkMDBQTZo0sSvz9/dX9erVbeUTJ07UoUOHNH/+fEnSiBEjNGvWLD3++ON68MEHtWnTJsXHx2vx4sWl9BIAAEB5VuorsGZkZGj//v22x3Xq1NGXX36p1NRUNW/eXC+99JLefvtt3X333aV9agAAUA5ZjPzZpGVYZmamgoODderUKQUFBZndHAAA4AJXP7+5Nw0AADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqYoVRmbPnq1mzZopKChIQUFBat26tb766qsC66empspisThsu3fvvuKGAwAA9+BZnMqRkZF6+eWXVa9ePUnShx9+qD59+mj79u1q3Lhxgfvt2bNHQUFBtsfXXXddCZsLAADcTbHCSO/eve0eT506VbNnz9bmzZsLDSMhISGqUqVKiRoIAADcW4nnjOTm5mrJkiU6e/asWrduXWjdG2+8UeHh4ercubNSUlKKPHZWVpYyMzPtNgAA4J6KHUZ27typgIAAeXt7a8SIEVq2bJkaNWrktG54eLjmzp2rpKQkJScnKyYmRp07d9a6desKPcf06dMVHBxs26KioorbTAAAUE5YDMMwirNDdna29u/frz///FNJSUn64IMPtHbt2gIDyeV69+4ti8WiL774osA6WVlZysrKsj3OzMxUVFSUTp06ZTf3BAAAlF2ZmZkKDg4u8vO7WHNGJKly5cq2CaytWrXS1q1b9dZbb+m9995zaf9bbrlFCxcuLLSOt7e3vL29i9s0AABQDl3xOiOGYdj1YhRl+/btCg8Pv9LTAgAAN1GsnpFJkybpjjvuUFRUlE6fPq0lS5YoNTVVX3/9tSRp4sSJOnTokObPny9JmjlzpqKjo9W4cWNlZ2dr4cKFSkpKUlJSUum/EgAAUC4VK4z8/vvvuv/++5WRkaHg4GA1a9ZMX3/9tbp06SJJysjI0P79+231s7OzNW7cOB06dEi+vr5q3LixVqxYoR49epTuqwAAAOVWsSewmsHVCTAAAKDscPXzm3vTAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExVrDAye/ZsNWvWTEFBQQoKClLr1q311VdfFbrP2rVr1bJlS/n4+Khu3bqaM2fOFTUYAAC4l2KFkcjISL388sv6/vvv9f333+u2225Tnz599NNPPzmtn56erh49eqhdu3bavn27Jk2apEcffVRJSUml0ngAAFD+WQzDMK7kANWqVdNrr72moUOHOjw3YcIEffHFF9q1a5etbMSIEdqxY4c2bdrk8jkyMzMVHBysU6dOKSgo6EqaCwAArhFXP79LPGckNzdXS5Ys0dmzZ9W6dWundTZt2qSuXbvalXXr1k3ff/+9Ll68WOCxs7KylJmZabcBAAD3VOwwsnPnTgUEBMjb21sjRozQsmXL1KhRI6d1jxw5otDQULuy0NBQ5eTk6Pjx4wWeY/r06QoODrZtUVFRxW0mAAAoJ4odRmJiYpSWlqbNmzfr4YcfVlxcnP79738XWN9isdg9zh8Vurz8UhMnTtSpU6ds24EDB4rbTAAAUE54FneHypUrq169epKkVq1aaevWrXrrrbf03nvvOdQNCwvTkSNH7MqOHj0qT09PVa9evcBzeHt7y9vbu7hNAwAA5dAVrzNiGIaysrKcPte6dWutWrXKrmzlypVq1aqVvLy8rvTUAADADRQrjEyaNEnr16/X3r17tXPnTj399NNKTU3VwIEDJVmHVx544AFb/REjRmjfvn16/PHHtWvXLiUkJCg+Pl7jxo0r3VcBAADKrWIN0/z++++6//77lZGRoeDgYDVr1kxff/21unTpIknKyMjQ/v37bfXr1KmjL7/8Uo899pjeeecdRURE6O2339bdd99duq8CAACUW1e8zsi1wDojAACUP1d9nREAAIDSQBgBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApiKMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABTEUYAAICpCCMAAMBUhBEAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYCrCCAAAMBVhBAAAmIowAgAATEUYAQAApqrQYcQwpGPHzG4FAAAVW4UOI++9JzVoIH3+udktAQCg4qqwYSQvT1q0SDpxQurbVxo5Ujp/3uxWAQBQ8VTYMFKpkvTtt9K4cdbH774r3Xyz9NNP5rYLAICKpsKGEUmqXFl67TXp66+l0FDpxx+lVq2kOXOs80kAAMDVV6HDSL5u3aQdO6xfL1yQHn5Yuvtu6xAOAAC4uggj/xUaKn35pTRjhuTlJS1bJt1wg7RundktAwDAvRFGLlGpkvT449KmTdL110sHD0qdOknPPy/l5JjdOgAA3BNhxImWLaUffpAGDbJedfPii1LHjtK+fWa3DAAA90MYKUBAgDRvnvTRR1JgoPR//2cdtvnkE7NbBgCAeyGMFGHAACktTfrrX6VTp6S//U166CHp7FmzWwYAgHsgjLigbl1p/Xpp4kTJYpHef996CfCOHWa3TMrNlVJTpcWLrV9zc81uEQAAxUMYcZGXlzRtmnWhtPBwafdua2/JP/5h3pokyclSdLR1ku2AAdav0dHWcgAAygvCSDHddpv0r39JvXpJWVnSo49KffpIx49f23YkJ0v33GO94udShw5ZywkkAIDygjBSAjVqSF98Ye0V8faWli+XmjWT1qy5NufPzZXGjHHeI5NfNnYsQzYAgPKBMFJCFos0apT03XfWO/9mZEi33y5NmiRdvHh1z71+vWOPyKUMQzpwwFoPAICyjjByhW64Qdq2zXqFjWFI06dL7dpJv/129c6ZkVG69QAAMBNhpBT4+UnvvWddg6RKFWtvSfPm1itcrobw8NKtBwCAmQgjpeiee6yX+7ZtK50+bb3CZfBg6cyZ0j1Pu3ZSZKR1qMgZi0WKirLWAwCgrCOMlLJataSUFOv9bCpVkhITpRYtrEM5pcXDQ3rrLeu/Lw8k+Y9nzrTWAwCgrCOMXAWentLkydZQEhkp/fyz1Lq19Y7AeXmlc45+/aRPP5Vq1rQvj4y0lvfrVzrnAQDgaitWGJk+fbpuuukmBQYGKiQkRH379tWePXsK3Sc1NVUWi8Vh27179xU1vDxo3946bHPXXdYrbMaNk3r0kH7/vXSO36+ftHevNfQsWmT9mp5OEAEAlC/FCiNr167VyJEjtXnzZq1atUo5OTnq2rWrzrpwo5Y9e/YoIyPDtl1//fUlbnR5Uq2alJQkzZkj+fhI33xjXZPkm29K5/geHtY7Cvfvb/3K0AwAoLyxGEbJFzM/duyYQkJCtHbtWrVv395pndTUVHXq1EknT55UlSpVSnSezMxMBQcH69SpUwoKCippc03300/S3/8u/fij9fETT1iXmK9c2dx2AQBwNbj6+X1Fc0ZOnTolSapWrVqRdW+88UaFh4erc+fOSklJKbRuVlaWMjMz7TZ30LixtGWLNHKk9fGMGVKbNtY5JQAAVFQlDiOGYejxxx9X27Zt1aRJkwLrhYeHa+7cuUpKSlJycrJiYmLUuXNnrVu3rsB9pk+fruDgYNsWFRVV0maWOb6+0qxZ0mefWYdwtm2TbrxRmj/fvBvuAQBgphIP04wcOVIrVqzQhg0bFBkZWax9e/fuLYvFoi+++MLp81lZWcrKyrI9zszMVFRUVLkfprncwYPS/fdLqanWxwMGSLNnS270EgEAZVBOjnTihPTHH//bbr659BfLdHWYxrMkBx89erS++OILrVu3rthBRJJuueUWLVy4sMDnvb295e3tXZKmlSuRkdK330ovv2xdl2TRImnzZuvXv/7V7NYBAMo6w5AyMx2DRf7mrPzECem/syzsfPKJdfFOMxQrjBiGodGjR2vZsmVKTU1VnTp1SnTS7du3K5y1yiVZr355+mnpttusV8T89pt1BdeXXpLGj7cunAYAcH9ZWa4Ficsf5+SU/JxVqkjVq1s3P79SeynFVqwwMnLkSC1atEiff/65AgMDdeTIEUlScHCwfH19JUkTJ07UoUOHNH/+fEnSzJkzFR0drcaNGys7O1sLFy5UUlKSkpKSSvmllG+tW0tpadKIEdLSpdLEidZek/nzpYgIs1sHAHBVXp7055/F66n44w/JhVUyCuTraw0U1ar9L1zkb87Kqle3BhHPEo2PlL5iNWP27NmSpI4dO9qVz5s3T4MGDZIkZWRkaP/+/bbnsrOzNW7cOB06dEi+vr5q3LixVqxYoR49elxZy91QlSrWm+t17SqNHi2tXm29K/C8eVKvXma3DgAqtjNnpH37rNv+/davhw87houTJ0t+QUKlSo7hwZWA8d/+gHLritYZuVbcZZ2R4tizx7omSVqa9fGjj0qvvGJdOM1d5eZK69dLGRnWSVTt2rGIG9xHdrZ1bP/S7fRpx7Kiyr28rPPNata0fnX276Cggm+kCecMQzp+3D5oXL6dOFG8YwYGFtwrUVDACA52r+F5Vz+/CSNlWFaW9NRT1pveSdZeksWLpYYNTW3WVZGcLI0ZY73CKF9kpPWGgCxvD7Pk5Vm7zosKCa4Ei0suELzqAgKKDiw1arjXh15RcnOlQ4cKDhr790vnzhV9nCpVpNq1rTdFrV3b+v2sUcN5bwYLWhJG3MqXX0qDBknHjlm74t5+Wxo61H3+8klOts7gvvwnMf/1mX3jv5wc6fx56y+qS78WVnbhglS1qv2HQEgIPT3XWna2taft8GHrX7WuhIdLnzt9uvTX//Hzs/ZcXLoFBjqWOXsuMNAaag4etG6HDtl/PXjQOlfBFZUrW+ejFRRWIiOtPZRlZU5BUS5cKDho7Ntn/d7k5hZ9nPDw/wUNZ1sF/Ai6IoQRN5ORIT3wgHVSqyTde680d641pZdnublSdLR9j8ilLBbrL8X0dPsP8osXHYNAYSGhpM+dP289V2nw8LD+8s//Re/sa0SEew/FlZbcXOnoUWvIyN8OHXJ8fPx46ZzPw6PkAeLSLSDg6n+4nz3rPKRcWvb7766FrEqVpNDQwgNLzZrXZr7Cn38WHDT27bP+PBTF01OKiio4aERFSRVgVYlrijDihvLyrEvIT5pk/Wu9Vi3rmiS33mp2y+zl5Fh/IV66nTvnWHb2rPU+PQkJRR8zNNT6yzM/JLjyF87V4Ov7v83Pz/7rpf/29rZOZMv/ADhyxPr+uaJGDcdf9pcHF3edE2AY1sl/BYWL/H8fOeL6z0Dlyta/dq+7rmQBIjDQ+p660/c7v8eooLCS/9XVS0arVXMeUi4tCw4u+HuYl2cNSAUNn+zbZ+2pKoq/f8FBo3ZtKSyM3slrjTDixrZssa7W+uuv1r9cJk+2BhRX/5MZhrVL01k4KCo8uFIvO/uqvnw7FkvRwaCwsuLU9/Yu+Rh7To71A9TZL/xLv7o6r8Dfv+DelfyvISFla07AmTNF92QcPuz69yD/r/b8HqX87fLH1au7V5C4VvLyrEPDl4eVy//tyjwLyfFn1tPTPnC48nujRo2Cg0atWtZQxHtdthBG3FxmpvWGe/kL2d56q9SypesB4lq865UqWX8B5W9+fvaP/f2tr2PFiqKP9c471qtrLg8IlSu7zy8fw7DOaygqsLg6J8DT839zAgoKLRERVz7JLivrf0GrsLBRnPtdVq/uPFhcWhYSUn7mM7grw7Cu5FnYkNDBg65dhVKpkvW9LSho1Kpl/Z2B8oUwUkEsWCA98oj1r86S8PZ2DAhFBQhX63l7Fx0U8ueMHDrkPCAVNGekIsufE+AsrFw6LOTq/+yQEOdhJX87f955wMj/d3HmZQQGFt6LUbOmtSudeTPu5dw5689Kflg5eNDaW3jpVSk1a1ovW4Z7IYxUIL/+al0YzTCKHx7Kwgd8/tU0kv0HaFm5mqY8unjRtWGh0hpSy78yo6ghk8DA0jkfgPKBMIJyxdk6I1FR1jVWCCJXh2FYJ9le3qtyeU+Ln1/RQYOxegDOEEZQ7rjLCqzu8joA4Eq5+vnN9C+UGR4e0mW3PSp3WEkWAIqvDF34B5Rv+XNfLl/A7dAha3lysjntAoCyjjAClILcXGuPiLNBz/yysWPNW6wNAMoywghQCtavL3hJe8kaSA4csNYDANgjjAClICOjdOsBQEVCGAFKQXh46dYDgIqEq2mAUtCunfWqmaJWkm3X7tq3raS4RBnAtULPCFAKPDysl+9Kjot/5T+eObP8fJgnJ1uX6e/UyXpTxk6drI+5IgjA1UAYAUpJv37Wpetr1rQvj4wsX0vac4kygGuNFViBUlaehzfyb1xY0JVB3LgQQHGwAitgkvK8kmxxLlEur68RQNnDMA0AGy5RBmAGekYA2LjjJcrledgMqCjoGQFgk3+J8uVXBOWzWKSoqPJziTJXBQHlA2EEgI07XaLMVUFA+UEYAWDHHS5R5saFQPnCnBEADvr1k/r0Kb9zLdzxqiDmvsCdEUYAOFWeL1F2t6uCkpOtPT2XBqzISOuQWnnoqQKKwjANALfjTlcFMfcFFQErsAJwO/kryRZ148KyvpKsO66Iy3BTxeLq5zc9IwDcjrtcFVScuS/lAZdaoyCEEQBuyR2uCnKnuS8MN6EwTGAF4LbK+1VB7jL3pahLrS0W66XWffqUn/eG4abSRRgB4NbK81VB+SviFjX3payviOtul1pzdVPpY5gGAMood5n7wnBT2ZWbK6WmSosXW7+atRAgYQQAyjB3mPtSUYabpPK1sm9ZmlDMpb0AUA6U5zkK7nKpdWqq9QO7KCkpZX+4Kb+H5/L3I7/HrbSCLpf2AoAbyZ/70r+/9WtZ/tC+HMNNZUtZ7OEhjAAArjqGm8qOsrh+DVfTAACuifJ+qbW7XN1UFnt4CCMAgGumPF9qnT/cdM891uBxaSApT8NNZbGHh2EaAABc5A7DTfk9PJfP38lnsUhRUde2h4eeEQAAiqG8DzeVxR4ewggAAMVUnoebpP/18DhbSXbmzGvfw0MYAQCgAipLPTyEEQAAKqiy0sPDBFYAAGAqwggAADAVYQQAAJiKMAIAAExFGAEAAKYijAAAAFMRRgAAgKkIIwAAwFSEEQAAYKpysQKr8d+7+GRmZprcEgAA4Kr8z23j0rvxOVEuwsjp06clSVFRUSa3BAAAFNfp06cVHBxc4PMWo6i4Ugbk5eXp8OHDCgwMlCX//sawyczMVFRUlA4cOKCgoCCzmwPxnpQ1vB9lC+9H2XI13w/DMHT69GlFRESoUqWCZ4aUi56RSpUqKTIy0uxmlHlBQUH8xy5jeE/KFt6PsoX3o2y5Wu9HYT0i+ZjACgAATEUYAQAApiKMuAFvb289//zz8vb2Nrsp+C/ek7KF96Ns4f0oW8rC+1EuJrACAAD3Rc8IAAAwFWEEAACYijACAABMRRgBAACmIowAAABTEUbKsenTp+umm25SYGCgQkJC1LdvX+3Zs8fsZuG/pk+fLovForFjx5rdlArr0KFDio2NVfXq1eXn56fmzZtr27ZtZjerwsrJydEzzzyjOnXqyNfXV3Xr1tWLL76ovLw8s5tWIaxbt069e/dWRESELBaLPvvsM7vnDcPQ5MmTFRERIV9fX3Xs2FE//fTTNWkbYaQcW7t2rUaOHKnNmzdr1apVysnJUdeuXXX27Fmzm1bhbd26VXPnzlWzZs3MbkqFdfLkSd16663y8vLSV199pX//+9+aMWOGqlSpYnbTKqxXXnlFc+bM0axZs7Rr1y69+uqreu211/SPf/zD7KZVCGfPntUNN9ygWbNmOX3+1Vdf1RtvvKFZs2Zp69atCgsLU5cuXWw3q72aWGfEjRw7dkwhISFau3at2rdvb3ZzKqwzZ86oRYsWevfddzVlyhQ1b95cM2fONLtZFc5TTz2l//u//9P69evNbgr+q1evXgoNDVV8fLyt7O6775afn58WLFhgYssqHovFomXLlqlv376SrL0iERERGjt2rCZMmCBJysrKUmhoqF555RUNHz78qraHnhE3curUKUlStWrVTG5JxTZy5Ej17NlTt99+u9lNqdC++OILtWrVSvfee69CQkJ044036v333ze7WRVa27ZttXr1av3nP/+RJO3YsUMbNmxQjx49TG4Z0tPTdeTIEXXt2tVW5u3trQ4dOmjjxo1X/fzl4q69KJphGHr88cfVtm1bNWnSxOzmVFhLlizRDz/8oK1bt5rdlArvt99+0+zZs/X4449r0qRJ2rJlix599FF5e3vrgQceMLt5FdKECRN06tQpNWjQQB4eHsrNzdXUqVPVv39/s5tW4R05ckSSFBoaalceGhqqffv2XfXzE0bcxKhRo/Svf/1LGzZsMLspFdaBAwc0ZswYrVy5Uj4+PmY3p8LLy8tTq1atNG3aNEnSjTfeqJ9++kmzZ88mjJhk6dKlWrhwoRYtWqTGjRsrLS1NY8eOVUREhOLi4sxuHmQdvrmUYRgOZVcDYcQNjB49Wl988YXWrVunyMhIs5tTYW3btk1Hjx5Vy5YtbWW5ublat26dZs2apaysLHl4eJjYwoolPDxcjRo1sitr2LChkpKSTGoRnnzyST311FP6+9//Lklq2rSp9u3bp+nTpxNGTBYWFibJ2kMSHh5uKz969KhDb8nVwJyRcswwDI0aNUrJyclas2aN6tSpY3aTKrTOnTtr586dSktLs22tWrXSwIEDlZaWRhC5xm699VaHS93/85//qHbt2ia1COfOnVOlSvYfOx4eHlzaWwbUqVNHYWFhWrVqla0sOztba9euVZs2ba76+ekZKcdGjhypRYsW6fPPP1dgYKBtzC84OFi+vr4mt67iCQwMdJiv4+/vr+rVqzOPxwSPPfaY2rRpo2nTpulvf/ubtmzZorlz52ru3LlmN63C6t27t6ZOnapatWqpcePG2r59u9544w0NGTLE7KZVCGfOnNEvv/xie5yenq60tDRVq1ZNtWrV0tixYzVt2jRdf/31uv766zVt2jT5+flpwIABV79xBsotSU63efPmmd00/FeHDh2MMWPGmN2MCmv58uVGkyZNDG9vb6NBgwbG3LlzzW5ShZaZmWmMGTPGqFWrluHj42PUrVvXePrpp42srCyzm1YhpKSkOP3MiIuLMwzDMPLy8oznn3/eCAsLM7y9vY327dsbO3fuvCZtY50RAABgKuaMAAAAUxFGAACAqQgjAADAVIQRAABgKsIIAAAwFWEEAACYijACAABMRRgBAACmIowAAABTEUYAAICpCCMAAMBU/w8DKi32AkQauwAAAABJRU5ErkJggg==",
+ "image/png": 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",
"text/plain": [
"<Figure size 640x480 with 1 Axes>"
]
@@ -1065,7 +1037,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 22,
"metadata": {
"colab_type": "code"
},
@@ -1110,7 +1082,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 23,
"metadata": {
"colab_type": "code"
},
@@ -1132,11 +1104,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 24,
"metadata": {
"colab_type": "code"
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(None, 16)\n"
+ ]
+ }
+ ],
"source": [
"num_features = 14\n",
"steps = 120\n",
@@ -1156,11 +1136,19 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 25,
"metadata": {
"colab_type": "code"
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(None, 120, 16)\n"
+ ]
+ }
+ ],
"source": [
"num_features = 14\n",
"steps = 120\n",
@@ -1180,7 +1168,7 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 26,
"metadata": {
"colab_type": "code"
},
@@ -1230,11 +1218,218 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 27,
"metadata": {
"colab_type": "code"
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/50\n",
+ "819/819 [==============================] - ETA: 0s - loss: 26.7029 - mae: 3.8139INFO:tensorflow:Assets written to: jena_lstm_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: jena_lstm_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "819/819 [==============================] - 155s 185ms/step - loss: 26.7029 - mae: 3.8139 - val_loss: 9.6522 - val_mae: 2.4091\n",
+ "Epoch 2/50\n",
+ "819/819 [==============================] - ETA: 0s - loss: 14.8421 - mae: 2.9920INFO:tensorflow:Assets written to: jena_lstm_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: jena_lstm_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "819/819 [==============================] - 152s 185ms/step - loss: 14.8421 - mae: 2.9920 - val_loss: 9.4700 - val_mae: 2.3839\n",
+ "Epoch 3/50\n",
+ "819/819 [==============================] - ETA: 0s - loss: 13.8937 - mae: 2.8955INFO:tensorflow:Assets written to: jena_lstm_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: jena_lstm_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "819/819 [==============================] - 152s 186ms/step - loss: 13.8937 - mae: 2.8955 - val_loss: 9.1903 - val_mae: 2.3493\n",
+ "Epoch 4/50\n",
+ "819/819 [==============================] - ETA: 0s - loss: 13.3374 - mae: 2.8367INFO:tensorflow:Assets written to: jena_lstm_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: jena_lstm_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "819/819 [==============================] - 152s 186ms/step - loss: 13.3374 - mae: 2.8367 - val_loss: 9.0649 - val_mae: 2.3395\n",
+ "Epoch 5/50\n",
+ "819/819 [==============================] - 151s 185ms/step - loss: 12.8392 - mae: 2.7839 - val_loss: 9.1294 - val_mae: 2.3468\n",
+ "Epoch 6/50\n",
+ "819/819 [==============================] - 150s 184ms/step - loss: 12.4934 - mae: 2.7431 - val_loss: 9.1956 - val_mae: 2.3574\n",
+ "Epoch 7/50\n",
+ "819/819 [==============================] - 151s 184ms/step - loss: 12.2608 - mae: 2.7174 - val_loss: 9.1389 - val_mae: 2.3521\n",
+ "Epoch 8/50\n",
+ "819/819 [==============================] - 151s 184ms/step - loss: 11.9583 - mae: 2.6862 - val_loss: 9.1629 - val_mae: 2.3422\n",
+ "Epoch 9/50\n",
+ "819/819 [==============================] - 150s 183ms/step - loss: 11.7617 - mae: 2.6638 - val_loss: 9.3368 - val_mae: 2.3714\n",
+ "Epoch 10/50\n",
+ "819/819 [==============================] - 151s 184ms/step - loss: 11.5716 - mae: 2.6439 - val_loss: 9.3181 - val_mae: 2.3783\n",
+ "Epoch 11/50\n",
+ "819/819 [==============================] - 151s 184ms/step - loss: 11.4004 - mae: 2.6241 - val_loss: 9.3491 - val_mae: 2.3704\n",
+ "Epoch 12/50\n",
+ "819/819 [==============================] - 151s 184ms/step - loss: 11.2762 - mae: 2.6084 - val_loss: 9.2386 - val_mae: 2.3561\n",
+ "Epoch 13/50\n",
+ "819/819 [==============================] - 151s 184ms/step - loss: 11.1278 - mae: 2.5897 - val_loss: 9.2087 - val_mae: 2.3475\n",
+ "Epoch 14/50\n",
+ "819/819 [==============================] - ETA: 0s - loss: 11.0411 - mae: 2.5792INFO:tensorflow:Assets written to: jena_lstm_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: jena_lstm_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "819/819 [==============================] - 151s 185ms/step - loss: 11.0411 - mae: 2.5792 - val_loss: 9.0596 - val_mae: 2.3311\n",
+ "Epoch 15/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 10.8994 - mae: 2.5632 - val_loss: 9.2923 - val_mae: 2.3586\n",
+ "Epoch 16/50\n",
+ "819/819 [==============================] - 150s 183ms/step - loss: 10.8133 - mae: 2.5520 - val_loss: 9.0753 - val_mae: 2.3266\n",
+ "Epoch 17/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 10.6568 - mae: 2.5328 - val_loss: 9.5394 - val_mae: 2.4008\n",
+ "Epoch 18/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 10.5674 - mae: 2.5230 - val_loss: 9.3116 - val_mae: 2.3654\n",
+ "Epoch 19/50\n",
+ "819/819 [==============================] - 151s 183ms/step - loss: 10.5351 - mae: 2.5157 - val_loss: 9.5253 - val_mae: 2.3999\n",
+ "Epoch 20/50\n",
+ "819/819 [==============================] - 150s 183ms/step - loss: 10.4634 - mae: 2.5061 - val_loss: 9.3950 - val_mae: 2.3726\n",
+ "Epoch 21/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 10.3274 - mae: 2.4946 - val_loss: 9.5900 - val_mae: 2.3969\n",
+ "Epoch 22/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 10.2619 - mae: 2.4850 - val_loss: 9.8298 - val_mae: 2.4416\n",
+ "Epoch 23/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 10.1437 - mae: 2.4693 - val_loss: 9.7041 - val_mae: 2.4099\n",
+ "Epoch 24/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 10.0374 - mae: 2.4604 - val_loss: 9.6310 - val_mae: 2.4089\n",
+ "Epoch 25/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.9716 - mae: 2.4491 - val_loss: 9.8649 - val_mae: 2.4417\n",
+ "Epoch 26/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.9349 - mae: 2.4439 - val_loss: 9.6900 - val_mae: 2.4259\n",
+ "Epoch 27/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.9030 - mae: 2.4396 - val_loss: 9.6753 - val_mae: 2.4231\n",
+ "Epoch 28/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.8589 - mae: 2.4338 - val_loss: 9.8820 - val_mae: 2.4419\n",
+ "Epoch 29/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.7599 - mae: 2.4195 - val_loss: 9.6898 - val_mae: 2.4171\n",
+ "Epoch 30/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.6880 - mae: 2.4123 - val_loss: 10.0409 - val_mae: 2.4621\n",
+ "Epoch 31/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.6058 - mae: 2.4052 - val_loss: 10.2813 - val_mae: 2.4933\n",
+ "Epoch 32/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.5928 - mae: 2.3992 - val_loss: 10.1269 - val_mae: 2.4682\n",
+ "Epoch 33/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.5164 - mae: 2.3906 - val_loss: 10.5991 - val_mae: 2.5307\n",
+ "Epoch 34/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.5286 - mae: 2.3908 - val_loss: 10.1711 - val_mae: 2.4934\n",
+ "Epoch 35/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.4413 - mae: 2.3771 - val_loss: 10.2889 - val_mae: 2.4983\n",
+ "Epoch 36/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.4400 - mae: 2.3791 - val_loss: 10.4841 - val_mae: 2.5184\n",
+ "Epoch 37/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.3416 - mae: 2.3662 - val_loss: 10.2402 - val_mae: 2.4826\n",
+ "Epoch 38/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.3668 - mae: 2.3662 - val_loss: 10.4641 - val_mae: 2.5193\n",
+ "Epoch 39/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.3191 - mae: 2.3620 - val_loss: 10.6007 - val_mae: 2.5446\n",
+ "Epoch 40/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.2655 - mae: 2.3558 - val_loss: 10.5911 - val_mae: 2.5278\n",
+ "Epoch 41/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.2330 - mae: 2.3522 - val_loss: 10.4439 - val_mae: 2.5336\n",
+ "Epoch 42/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.2052 - mae: 2.3483 - val_loss: 10.5186 - val_mae: 2.5323\n",
+ "Epoch 43/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.1347 - mae: 2.3379 - val_loss: 10.3882 - val_mae: 2.5237\n",
+ "Epoch 44/50\n",
+ "819/819 [==============================] - 148s 181ms/step - loss: 9.1186 - mae: 2.3388 - val_loss: 10.6812 - val_mae: 2.5575\n",
+ "Epoch 45/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.0932 - mae: 2.3337 - val_loss: 10.6406 - val_mae: 2.5473\n",
+ "Epoch 46/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.0489 - mae: 2.3309 - val_loss: 10.3736 - val_mae: 2.5039\n",
+ "Epoch 47/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 8.9889 - mae: 2.3220 - val_loss: 10.5870 - val_mae: 2.5298\n",
+ "Epoch 48/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 9.0355 - mae: 2.3264 - val_loss: 10.6290 - val_mae: 2.5477\n",
+ "Epoch 49/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 8.9657 - mae: 2.3190 - val_loss: 10.4987 - val_mae: 2.5304\n",
+ "Epoch 50/50\n",
+ "819/819 [==============================] - 149s 182ms/step - loss: 8.9442 - mae: 2.3132 - val_loss: 10.8503 - val_mae: 2.5739\n",
+ "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer lstm_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "405/405 [==============================] - 13s 31ms/step - loss: 10.0335 - mae: 2.5020\n",
+ "Test MAE: 2.50\n"
+ ]
+ }
+ ],
"source": [
"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\n",
"x = layers.LSTM(32, recurrent_dropout=0.25)(inputs)\n",
@@ -1260,12 +1455,12 @@
"cell_type": "markdown",
"metadata": {},
"source": [
- "Let's try the same thin on the CPUs. To save time, we will reduce the number of epochs to 5."
+ "Let's try the same thing on the CPUs. To save time, we will reduce the number of epochs to 5."
]
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 28,
"metadata": {
"tags": []
},
@@ -1276,11 +1471,146 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 29,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/5\n",
+ " 3/819 [..............................] - ETA: 49s - loss: 156.4061 - mae: 10.4026 "
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "2025-03-31 16:36:00.988119: I external/local_xla/xla/service/service.cc:168] XLA service 0x7f84f4124ed0 initialized for platform Host (this does not guarantee that XLA will be used). Devices:\n",
+ "2025-03-31 16:36:00.988423: I external/local_xla/xla/service/service.cc:176] StreamExecutor device (0): Host, Default Version\n",
+ "2025-03-31 16:36:01.034771: E external/local_xla/xla/stream_executor/stream_executor_internal.h:177] SetPriority unimplemented for this stream.\n",
+ "2025-03-31 16:36:01.039144: E external/local_xla/xla/stream_executor/stream_executor_internal.h:177] SetPriority unimplemented for this stream.\n",
+ "2025-03-31 16:36:01.105169: E external/local_xla/xla/stream_executor/stream_executor_internal.h:177] SetPriority unimplemented for this stream.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "818/819 [============================>.] - ETA: 0s - loss: 29.2078 - mae: 3.9820INFO:tensorflow:Assets written to: jena_lstm_dropout_cpu.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: jena_lstm_dropout_cpu.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "819/819 [==============================] - 66s 68ms/step - loss: 29.2002 - mae: 3.9815 - val_loss: 9.7682 - val_mae: 2.4340\n",
+ "Epoch 2/5\n",
+ "818/819 [============================>.] - ETA: 0s - loss: 14.8603 - mae: 2.9900INFO:tensorflow:Assets written to: jena_lstm_dropout_cpu.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: jena_lstm_dropout_cpu.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "819/819 [==============================] - 56s 68ms/step - loss: 14.8600 - mae: 2.9900 - val_loss: 9.5812 - val_mae: 2.4108\n",
+ "Epoch 3/5\n",
+ "818/819 [============================>.] - ETA: 0s - loss: 13.8221 - mae: 2.8791INFO:tensorflow:Assets written to: jena_lstm_dropout_cpu.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: jena_lstm_dropout_cpu.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "819/819 [==============================] - 56s 68ms/step - loss: 13.8226 - mae: 2.8792 - val_loss: 9.4247 - val_mae: 2.3883\n",
+ "Epoch 4/5\n",
+ "818/819 [============================>.] - ETA: 0s - loss: 13.1137 - mae: 2.8061INFO:tensorflow:Assets written to: jena_lstm_dropout_cpu.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: jena_lstm_dropout_cpu.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "819/819 [==============================] - 56s 68ms/step - loss: 13.1143 - mae: 2.8061 - val_loss: 9.3905 - val_mae: 2.3853\n",
+ "Epoch 5/5\n",
+ "818/819 [============================>.] - ETA: 0s - loss: 12.6629 - mae: 2.7527INFO:tensorflow:Assets written to: jena_lstm_dropout_cpu.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: jena_lstm_dropout_cpu.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "819/819 [==============================] - 57s 69ms/step - loss: 12.6635 - mae: 2.7528 - val_loss: 9.1294 - val_mae: 2.3502\n",
+ "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer lstm_2 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "405/405 [==============================] - 7s 17ms/step - loss: 10.0401 - mae: 2.4960\n",
+ "Test MAE: 2.50\n"
+ ]
+ }
+ ],
"source": [
"with tf.device('/CPU:0'):\n",
" inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\n",
@@ -1305,11 +1635,22 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 30,
"metadata": {
"tags": []
},
- "outputs": [],
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "<Figure size 640x480 with 1 Axes>"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
"source": [
"import matplotlib.pyplot as plt\n",
"loss = history.history[\"mae\"]\n",
@@ -1344,11 +1685,46 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 31,
"metadata": {
"colab_type": "code"
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer lstm_3 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer lstm_3 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/50\n"
+ ]
+ },
+ {
+ "ename": "ValueError",
+ "evalue": "in user code:\n\n File \"/gpfs/opt/sw/jupyterhub/envs/conda/vsc5/jupyterhub-huggingface-v2/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1401, in train_function *\n return step_function(self, iterator)\n File \"/gpfs/opt/sw/jupyterhub/envs/conda/vsc5/jupyterhub-huggingface-v2/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1384, in step_function **\n outputs = model.distribute_strategy.run(run_step, args=(data,))\n File \"/gpfs/opt/sw/jupyterhub/envs/conda/vsc5/jupyterhub-huggingface-v2/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1373, in run_step **\n outputs = model.train_step(data)\n File \"/gpfs/opt/sw/jupyterhub/envs/conda/vsc5/jupyterhub-huggingface-v2/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1150, in train_step\n y_pred = self(x, training=True)\n File \"/gpfs/opt/sw/jupyterhub/envs/conda/vsc5/jupyterhub-huggingface-v2/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 70, in error_handler\n raise e.with_traceback(filtered_tb) from None\n File \"/gpfs/opt/sw/jupyterhub/envs/conda/vsc5/jupyterhub-huggingface-v2/lib/python3.10/site-packages/keras/src/backend.py\", line 4856, in rnn\n raise ValueError(\"Unrolling requires a fixed number of timesteps.\")\n\n ValueError: Exception encountered when calling layer 'lstm_3' (type LSTM).\n \n Unrolling requires a fixed number of timesteps.\n \n Call arguments received by layer 'lstm_3' (type LSTM):\n • inputs=tf.Tensor(shape=(None, None, 14), dtype=float32)\n • mask=None\n • training=True\n • initial_state=None\n",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[0;31mValueError\u001b[0m Traceback (most recent call last)",
+ "Cell \u001b[0;32mIn[31], line 12\u001b[0m\n\u001b[1;32m 7\u001b[0m callbacks \u001b[38;5;241m=\u001b[39m [\n\u001b[1;32m 8\u001b[0m keras\u001b[38;5;241m.\u001b[39mcallbacks\u001b[38;5;241m.\u001b[39mModelCheckpoint(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mjena_lstm_dropout_unrolled.tf\u001b[39m\u001b[38;5;124m\"\u001b[39m,\n\u001b[1;32m 9\u001b[0m save_best_only\u001b[38;5;241m=\u001b[39m\u001b[38;5;28;01mTrue\u001b[39;00m)\n\u001b[1;32m 10\u001b[0m ]\n\u001b[1;32m 11\u001b[0m model\u001b[38;5;241m.\u001b[39mcompile(optimizer\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mrmsprop\u001b[39m\u001b[38;5;124m\"\u001b[39m, loss\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmse\u001b[39m\u001b[38;5;124m\"\u001b[39m, metrics\u001b[38;5;241m=\u001b[39m[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mmae\u001b[39m\u001b[38;5;124m\"\u001b[39m])\n\u001b[0;32m---> 12\u001b[0m history \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mfit\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 13\u001b[0m \u001b[43m \u001b[49m\u001b[43mepochs\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m50\u001b[39;49m\u001b[43m,\u001b[49m\n\u001b[1;32m 14\u001b[0m \u001b[43m \u001b[49m\u001b[43mvalidation_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mval_dataset\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m 15\u001b[0m \u001b[43m \u001b[49m\u001b[43mcallbacks\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcallbacks\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m 17\u001b[0m model \u001b[38;5;241m=\u001b[39m keras\u001b[38;5;241m.\u001b[39mmodels\u001b[38;5;241m.\u001b[39mload_model(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mjena_lstm_dropout_unrolled.tf\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m 18\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mTest MAE: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mmodel\u001b[38;5;241m.\u001b[39mevaluate(test_dataset)[\u001b[38;5;241m1\u001b[39m]\u001b[38;5;132;01m:\u001b[39;00m\u001b[38;5;124m.2f\u001b[39m\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
+ "File \u001b[0;32m/gpfs/opt/sw/jupyterhub/envs/conda/vsc5/jupyterhub-huggingface-v2/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py:70\u001b[0m, in \u001b[0;36mfilter_traceback.<locals>.error_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 67\u001b[0m filtered_tb \u001b[38;5;241m=\u001b[39m _process_traceback_frames(e\u001b[38;5;241m.\u001b[39m__traceback__)\n\u001b[1;32m 68\u001b[0m \u001b[38;5;66;03m# To get the full stack trace, call:\u001b[39;00m\n\u001b[1;32m 69\u001b[0m \u001b[38;5;66;03m# `tf.debugging.disable_traceback_filtering()`\u001b[39;00m\n\u001b[0;32m---> 70\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m e\u001b[38;5;241m.\u001b[39mwith_traceback(filtered_tb) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m 71\u001b[0m \u001b[38;5;28;01mfinally\u001b[39;00m:\n\u001b[1;32m 72\u001b[0m \u001b[38;5;28;01mdel\u001b[39;00m filtered_tb\n",
+ "File \u001b[0;32m/tmp/__autograph_generated_file8xbckcxh.py:15\u001b[0m, in \u001b[0;36mouter_factory.<locals>.inner_factory.<locals>.tf__train_function\u001b[0;34m(iterator)\u001b[0m\n\u001b[1;32m 13\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m 14\u001b[0m do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mTrue\u001b[39;00m\n\u001b[0;32m---> 15\u001b[0m retval_ \u001b[38;5;241m=\u001b[39m ag__\u001b[38;5;241m.\u001b[39mconverted_call(ag__\u001b[38;5;241m.\u001b[39mld(step_function), (ag__\u001b[38;5;241m.\u001b[39mld(\u001b[38;5;28mself\u001b[39m), ag__\u001b[38;5;241m.\u001b[39mld(iterator)), \u001b[38;5;28;01mNone\u001b[39;00m, fscope)\n\u001b[1;32m 16\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m:\n\u001b[1;32m 17\u001b[0m do_return \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mFalse\u001b[39;00m\n",
+ "\u001b[0;31mValueError\u001b[0m: in user code:\n\n File \"/gpfs/opt/sw/jupyterhub/envs/conda/vsc5/jupyterhub-huggingface-v2/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1401, in train_function *\n return step_function(self, iterator)\n File \"/gpfs/opt/sw/jupyterhub/envs/conda/vsc5/jupyterhub-huggingface-v2/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1384, in step_function **\n outputs = model.distribute_strategy.run(run_step, args=(data,))\n File \"/gpfs/opt/sw/jupyterhub/envs/conda/vsc5/jupyterhub-huggingface-v2/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1373, in run_step **\n outputs = model.train_step(data)\n File \"/gpfs/opt/sw/jupyterhub/envs/conda/vsc5/jupyterhub-huggingface-v2/lib/python3.10/site-packages/keras/src/engine/training.py\", line 1150, in train_step\n y_pred = self(x, training=True)\n File \"/gpfs/opt/sw/jupyterhub/envs/conda/vsc5/jupyterhub-huggingface-v2/lib/python3.10/site-packages/keras/src/utils/traceback_utils.py\", line 70, in error_handler\n raise e.with_traceback(filtered_tb) from None\n File \"/gpfs/opt/sw/jupyterhub/envs/conda/vsc5/jupyterhub-huggingface-v2/lib/python3.10/site-packages/keras/src/backend.py\", line 4856, in rnn\n raise ValueError(\"Unrolling requires a fixed number of timesteps.\")\n\n ValueError: Exception encountered when calling layer 'lstm_3' (type LSTM).\n \n Unrolling requires a fixed number of timesteps.\n \n Call arguments received by layer 'lstm_3' (type LSTM):\n • inputs=tf.Tensor(shape=(None, None, 14), dtype=float32)\n • mask=None\n • training=True\n • initial_state=None\n"
+ ]
+ }
+ ],
"source": [
"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]), batch_size=256)\n",
"x = layers.LSTM(32, recurrent_dropout=0.2, unroll=True)(inputs)\n",
@@ -1393,11 +1769,218 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 32,
"metadata": {
"colab_type": "code"
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer gru will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer gru will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer gru_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer gru_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/50\n",
+ "819/819 [==============================] - ETA: 0s - loss: 27.3420 - mae: 3.8400INFO:tensorflow:Assets written to: jena_stacked_gru_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: jena_stacked_gru_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "819/819 [==============================] - 288s 349ms/step - loss: 27.3420 - mae: 3.8400 - val_loss: 9.3874 - val_mae: 2.3581\n",
+ "Epoch 2/50\n",
+ "819/819 [==============================] - ETA: 0s - loss: 14.1142 - mae: 2.9103INFO:tensorflow:Assets written to: jena_stacked_gru_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: jena_stacked_gru_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "819/819 [==============================] - 286s 349ms/step - loss: 14.1142 - mae: 2.9103 - val_loss: 9.2372 - val_mae: 2.3596\n",
+ "Epoch 3/50\n",
+ "819/819 [==============================] - ETA: 0s - loss: 13.2735 - mae: 2.8223INFO:tensorflow:Assets written to: jena_stacked_gru_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "INFO:tensorflow:Assets written to: jena_stacked_gru_dropout.tf/assets\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "819/819 [==============================] - 286s 349ms/step - loss: 13.2735 - mae: 2.8223 - val_loss: 8.7588 - val_mae: 2.2970\n",
+ "Epoch 4/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 12.6955 - mae: 2.7623 - val_loss: 9.0156 - val_mae: 2.3266\n",
+ "Epoch 5/50\n",
+ "819/819 [==============================] - 282s 345ms/step - loss: 12.2468 - mae: 2.7104 - val_loss: 9.0760 - val_mae: 2.3427\n",
+ "Epoch 6/50\n",
+ "819/819 [==============================] - 282s 344ms/step - loss: 11.8190 - mae: 2.6675 - val_loss: 8.7672 - val_mae: 2.2999\n",
+ "Epoch 7/50\n",
+ "819/819 [==============================] - 282s 344ms/step - loss: 11.4198 - mae: 2.6205 - val_loss: 9.0930 - val_mae: 2.3397\n",
+ "Epoch 8/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 11.1773 - mae: 2.5954 - val_loss: 8.9567 - val_mae: 2.3247\n",
+ "Epoch 9/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 10.8220 - mae: 2.5529 - val_loss: 9.0240 - val_mae: 2.3303\n",
+ "Epoch 10/50\n",
+ "819/819 [==============================] - 284s 346ms/step - loss: 10.5072 - mae: 2.5159 - val_loss: 9.4371 - val_mae: 2.3876\n",
+ "Epoch 11/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 10.2267 - mae: 2.4820 - val_loss: 9.2064 - val_mae: 2.3487\n",
+ "Epoch 12/50\n",
+ "819/819 [==============================] - 283s 346ms/step - loss: 9.9719 - mae: 2.4536 - val_loss: 9.1848 - val_mae: 2.3473\n",
+ "Epoch 13/50\n",
+ "819/819 [==============================] - 283s 346ms/step - loss: 9.7219 - mae: 2.4220 - val_loss: 9.4376 - val_mae: 2.3832\n",
+ "Epoch 14/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 9.4992 - mae: 2.3953 - val_loss: 9.8387 - val_mae: 2.4371\n",
+ "Epoch 15/50\n",
+ "819/819 [==============================] - 283s 346ms/step - loss: 9.2751 - mae: 2.3681 - val_loss: 10.2060 - val_mae: 2.4887\n",
+ "Epoch 16/50\n",
+ "819/819 [==============================] - 283s 346ms/step - loss: 9.1393 - mae: 2.3465 - val_loss: 10.5398 - val_mae: 2.5315\n",
+ "Epoch 17/50\n",
+ "819/819 [==============================] - 282s 345ms/step - loss: 8.9412 - mae: 2.3237 - val_loss: 10.3671 - val_mae: 2.5036\n",
+ "Epoch 18/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 8.9141 - mae: 2.3184 - val_loss: 10.5711 - val_mae: 2.5360\n",
+ "Epoch 19/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 8.7263 - mae: 2.2973 - val_loss: 10.3721 - val_mae: 2.5027\n",
+ "Epoch 20/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 8.5785 - mae: 2.2757 - val_loss: 11.5436 - val_mae: 2.6609\n",
+ "Epoch 21/50\n",
+ "819/819 [==============================] - 281s 343ms/step - loss: 8.4479 - mae: 2.2599 - val_loss: 10.3596 - val_mae: 2.5050\n",
+ "Epoch 22/50\n",
+ "819/819 [==============================] - 282s 344ms/step - loss: 8.3935 - mae: 2.2519 - val_loss: 10.8778 - val_mae: 2.5671\n",
+ "Epoch 23/50\n",
+ "819/819 [==============================] - 282s 344ms/step - loss: 8.2928 - mae: 2.2356 - val_loss: 11.3801 - val_mae: 2.6336\n",
+ "Epoch 24/50\n",
+ "819/819 [==============================] - 282s 344ms/step - loss: 8.2059 - mae: 2.2267 - val_loss: 10.7669 - val_mae: 2.5639\n",
+ "Epoch 25/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 8.0939 - mae: 2.2126 - val_loss: 11.0368 - val_mae: 2.5866\n",
+ "Epoch 26/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 8.0697 - mae: 2.2060 - val_loss: 10.9142 - val_mae: 2.5640\n",
+ "Epoch 27/50\n",
+ "819/819 [==============================] - 282s 344ms/step - loss: 7.9563 - mae: 2.1948 - val_loss: 11.0826 - val_mae: 2.5818\n",
+ "Epoch 28/50\n",
+ "819/819 [==============================] - 282s 344ms/step - loss: 7.8648 - mae: 2.1817 - val_loss: 11.4338 - val_mae: 2.6157\n",
+ "Epoch 29/50\n",
+ "819/819 [==============================] - 282s 344ms/step - loss: 7.8261 - mae: 2.1742 - val_loss: 12.5806 - val_mae: 2.7733\n",
+ "Epoch 30/50\n",
+ "819/819 [==============================] - 282s 345ms/step - loss: 7.7462 - mae: 2.1648 - val_loss: 11.6492 - val_mae: 2.6570\n",
+ "Epoch 31/50\n",
+ "819/819 [==============================] - 283s 346ms/step - loss: 7.7399 - mae: 2.1617 - val_loss: 11.5544 - val_mae: 2.6476\n",
+ "Epoch 32/50\n",
+ "819/819 [==============================] - 282s 344ms/step - loss: 7.7054 - mae: 2.1567 - val_loss: 12.1834 - val_mae: 2.7174\n",
+ "Epoch 33/50\n",
+ "819/819 [==============================] - 282s 344ms/step - loss: 7.6300 - mae: 2.1475 - val_loss: 12.2661 - val_mae: 2.7402\n",
+ "Epoch 34/50\n",
+ "819/819 [==============================] - 283s 346ms/step - loss: 7.6216 - mae: 2.1452 - val_loss: 12.2686 - val_mae: 2.7218\n",
+ "Epoch 35/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 7.5221 - mae: 2.1339 - val_loss: 12.1261 - val_mae: 2.7139\n",
+ "Epoch 36/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 7.4957 - mae: 2.1268 - val_loss: 12.2793 - val_mae: 2.7313\n",
+ "Epoch 37/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 7.4381 - mae: 2.1196 - val_loss: 12.5502 - val_mae: 2.7566\n",
+ "Epoch 38/50\n",
+ "819/819 [==============================] - 282s 345ms/step - loss: 7.4060 - mae: 2.1136 - val_loss: 12.6159 - val_mae: 2.7653\n",
+ "Epoch 39/50\n",
+ "819/819 [==============================] - 282s 345ms/step - loss: 7.3860 - mae: 2.1098 - val_loss: 12.6317 - val_mae: 2.7484\n",
+ "Epoch 40/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 7.3389 - mae: 2.1044 - val_loss: 12.3411 - val_mae: 2.7552\n",
+ "Epoch 41/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 7.3203 - mae: 2.1002 - val_loss: 13.0801 - val_mae: 2.8180\n",
+ "Epoch 42/50\n",
+ "819/819 [==============================] - 282s 344ms/step - loss: 7.2378 - mae: 2.0915 - val_loss: 12.3876 - val_mae: 2.7315\n",
+ "Epoch 43/50\n",
+ "819/819 [==============================] - 282s 345ms/step - loss: 7.2655 - mae: 2.0902 - val_loss: 12.8629 - val_mae: 2.7970\n",
+ "Epoch 44/50\n",
+ "819/819 [==============================] - 283s 345ms/step - loss: 7.2234 - mae: 2.0866 - val_loss: 12.5936 - val_mae: 2.7622\n",
+ "Epoch 45/50\n",
+ "819/819 [==============================] - 282s 344ms/step - loss: 7.1770 - mae: 2.0794 - val_loss: 13.0341 - val_mae: 2.8178\n",
+ "Epoch 46/50\n",
+ "819/819 [==============================] - 284s 346ms/step - loss: 7.1552 - mae: 2.0750 - val_loss: 12.4987 - val_mae: 2.7623\n",
+ "Epoch 47/50\n",
+ "819/819 [==============================] - 283s 346ms/step - loss: 7.1032 - mae: 2.0693 - val_loss: 12.9602 - val_mae: 2.8039\n",
+ "Epoch 48/50\n",
+ "819/819 [==============================] - 284s 346ms/step - loss: 7.0696 - mae: 2.0621 - val_loss: 12.9397 - val_mae: 2.7993\n",
+ "Epoch 49/50\n",
+ "819/819 [==============================] - 284s 347ms/step - loss: 7.0431 - mae: 2.0573 - val_loss: 12.8652 - val_mae: 2.8040\n",
+ "Epoch 50/50\n",
+ "819/819 [==============================] - 284s 346ms/step - loss: 7.0105 - mae: 2.0558 - val_loss: 12.9405 - val_mae: 2.8141\n",
+ "WARNING:tensorflow:Layer gru will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer gru will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer gru_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stderr",
+ "output_type": "stream",
+ "text": [
+ "WARNING:tensorflow:Layer gru_1 will not use cuDNN kernels since it doesn't meet the criteria. It will use a generic GPU kernel as fallback when running on GPU.\n"
+ ]
+ },
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "405/405 [==============================] - 24s 58ms/step - loss: 9.5927 - mae: 2.4249\n",
+ "Test MAE: 2.42\n"
+ ]
+ }
+ ],
"source": [
"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\n",
"x = layers.GRU(32, recurrent_dropout=0.5, return_sequences=True)(inputs)\n",
@@ -1443,11 +2026,38 @@
},
{
"cell_type": "code",
- "execution_count": null,
+ "execution_count": 33,
"metadata": {
"colab_type": "code"
},
- "outputs": [],
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "Epoch 1/10\n",
+ "819/819 [==============================] - 12s 13ms/step - loss: 27.5774 - mae: 3.7585 - val_loss: 10.0597 - val_mae: 2.4704\n",
+ "Epoch 2/10\n",
+ "819/819 [==============================] - 11s 13ms/step - loss: 9.2899 - mae: 2.3752 - val_loss: 9.6952 - val_mae: 2.4252\n",
+ "Epoch 3/10\n",
+ "819/819 [==============================] - 10s 13ms/step - loss: 8.2105 - mae: 2.2322 - val_loss: 10.1286 - val_mae: 2.4730\n",
+ "Epoch 4/10\n",
+ "819/819 [==============================] - 10s 13ms/step - loss: 7.6980 - mae: 2.1629 - val_loss: 9.6934 - val_mae: 2.4041\n",
+ "Epoch 5/10\n",
+ "819/819 [==============================] - 10s 13ms/step - loss: 7.3615 - mae: 2.1133 - val_loss: 9.8460 - val_mae: 2.4357\n",
+ "Epoch 6/10\n",
+ "819/819 [==============================] - 10s 13ms/step - loss: 7.0892 - mae: 2.0742 - val_loss: 10.3093 - val_mae: 2.4840\n",
+ "Epoch 7/10\n",
+ "819/819 [==============================] - 10s 12ms/step - loss: 6.8777 - mae: 2.0404 - val_loss: 10.2899 - val_mae: 2.4855\n",
+ "Epoch 8/10\n",
+ "819/819 [==============================] - 10s 12ms/step - loss: 6.6437 - mae: 2.0053 - val_loss: 10.6970 - val_mae: 2.5219\n",
+ "Epoch 9/10\n",
+ "819/819 [==============================] - 10s 13ms/step - loss: 6.4784 - mae: 1.9787 - val_loss: 10.7954 - val_mae: 2.5281\n",
+ "Epoch 10/10\n",
+ "819/819 [==============================] - 11s 13ms/step - loss: 6.3192 - mae: 1.9543 - val_loss: 11.2543 - val_mae: 2.5851\n"
+ ]
+ }
+ ],
"source": [
"inputs = keras.Input(shape=(sequence_length, raw_data.shape[-1]))\n",
"x = layers.Bidirectional(layers.LSTM(16))(inputs)\n",
@@ -1504,7 +2114,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/16_RNN_exercise.ipynb b/16_RNN_exercise.ipynb
index 3f9004565a5c935fb9213b5fe26bfad274ec96df..942dbc3b555c977ac83e8217dc0d8ba833ad093f 100644
--- a/16_RNN_exercise.ipynb
+++ b/16_RNN_exercise.ipynb
@@ -592,7 +592,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/17_multi_GPU_horovod.ipynb b/17_multi_GPU_horovod.ipynb
index a40e9b524020fd91f36c7c2e3cb22b4cdfa1f605..aa75d6d8959baf03f6ab565c5080136f18fc941d 100644
--- a/17_multi_GPU_horovod.ipynb
+++ b/17_multi_GPU_horovod.ipynb
@@ -508,7 +508,7 @@
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
- "version": "3.10.5"
+ "version": "3.10.14"
}
},
"nbformat": 4,
diff --git a/18_TF_distributed.ipynb b/18_TF_distributed.ipynb
index 4d6b75eb92bbe27593ae4e9c233118e62973176a..60b868b2e1ba8064717b5b130b475383a7a229b1 100644
--- a/18_TF_distributed.ipynb
+++ b/18_TF_distributed.ipynb
@@ -26,6 +26,7 @@
"#SBATCH --qos=zen3_0512_a100x2 # qos for training\n",
"#SBATCH --gres=gpu:2 # Number of GPUs per node\n",
"#SBATCH --output=./output/%x-%j.out # Output file\n",
+ "#SBATCH --reservation=jh_training_dl_a100_2\n",
"#SBATCH --time=00:10:00\n",
"\n",
"######################\n",