"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# extra code – this cell generates and saves Figure 10–10\n",
"\n",
"n_rows = 4\n",
"n_cols = 10\n",
"plt.figure(figsize=(n_cols * 1.2, n_rows * 1.2))\n",
"for row in range(n_rows):\n",
" for col in range(n_cols):\n",
" index = n_cols * row + col\n",
" plt.subplot(n_rows, n_cols, index + 1)\n",
" plt.imshow(X_train[index], cmap=\"binary\", interpolation=\"nearest\")\n",
" plt.axis('off')\n",
" plt.title(class_names[y_train[index]])\n",
"plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
"\n",
"save_fig(\"fashion_mnist_plot\")\n",
"plt.show()"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Creating the model using the Sequential API"
]
},
{
"cell_type": "code",
"execution_count": 20,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/madhavan/python-venv/lib/python3.13/site-packages/keras/src/layers/core/input_layer.py:27: UserWarning: Argument `input_shape` is deprecated. Use `shape` instead.\n",
" warnings.warn(\n",
"E0000 00:00:1774940212.237376 439194 cuda_platform.cc:52] failed call to cuInit: INTERNAL: CUDA error: Failed call to cuInit: UNKNOWN ERROR (303)\n"
]
}
],
"source": [
"tf.random.set_seed(42)\n",
"model = tf.keras.Sequential()\n",
"model.add(tf.keras.layers.InputLayer(input_shape=[28, 28]))\n",
"model.add(tf.keras.layers.Flatten())\n",
"model.add(tf.keras.layers.Dense(300, activation=\"relu\"))\n",
"model.add(tf.keras.layers.Dense(100, activation=\"relu\"))\n",
"model.add(tf.keras.layers.Dense(10, activation=\"softmax\"))"
]
},
{
"cell_type": "code",
"execution_count": 21,
"metadata": {},
"outputs": [
{
"name": "stderr",
"output_type": "stream",
"text": [
"/home/madhavan/python-venv/lib/python3.13/site-packages/keras/src/layers/reshaping/flatten.py:37: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
" super().__init__(**kwargs)\n"
]
}
],
"source": [
"# extra code – clear the session to reset the name counters\n",
"tf.keras.backend.clear_session()\n",
"tf.random.set_seed(42)\n",
"\n",
"model = tf.keras.Sequential([\n",
" tf.keras.layers.Flatten(input_shape=[28, 28]),\n",
" tf.keras.layers.Dense(300, activation=\"relu\"),\n",
" tf.keras.layers.Dense(100, activation=\"relu\"),\n",
" tf.keras.layers.Dense(10, activation=\"softmax\")\n",
"])"
]
},
{
"cell_type": "code",
"execution_count": 22,
"metadata": {},
"outputs": [
{
"data": {
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"Model: \"sequential\"\n",
"\n"
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"output_type": "display_data"
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"┃ Layer (type) ┃ Output Shape ┃ Param # ┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
"│ flatten (Flatten) │ (None, 784) │ 0 │\n",
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"│ dense_1 (Dense) │ (None, 100) │ 30,100 │\n",
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"│ dense_2 (Dense) │ (None, 10) │ 1,010 │\n",
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"┃\u001b[1m \u001b[0m\u001b[1mLayer (type) \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
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"│ flatten (\u001b[38;5;33mFlatten\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m784\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
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"│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m300\u001b[0m) │ \u001b[38;5;34m235,500\u001b[0m │\n",
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},
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"output_type": "display_data"
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{
"data": {
"text/html": [
" Total params: 266,610 (1.02 MB)\n",
"\n"
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"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
" Trainable params: 266,610 (1.02 MB)\n",
"\n"
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"text/plain": [
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m266,610\u001b[0m (1.02 MB)\n"
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"output_type": "display_data"
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"metadata": {},
"output_type": "display_data"
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],
"source": [
"model.summary()"
]
},
{
"cell_type": "code",
"execution_count": 23,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"You must install pydot (`pip install pydot`) for `plot_model` to work.\n"
]
}
],
"source": [
"# extra code – another way to display the model's architecture\n",
"tf.keras.utils.plot_model(model, \"my_fashion_mnist_model.png\", show_shapes=True)"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"[,\n",
" ,\n",
" ,\n",
" ]"
]
},
"execution_count": 24,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.layers"
]
},
{
"cell_type": "code",
"execution_count": 25,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'dense'"
]
},
"execution_count": 25,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"hidden1 = model.layers[1]\n",
"hidden1.name"
]
},
{
"cell_type": "code",
"execution_count": 26,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"True"
]
},
"execution_count": 26,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.get_layer('dense') is hidden1"
]
},
{
"cell_type": "code",
"execution_count": 27,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[-0.01503324, -0.01729074, -0.0209767 , ..., 0.01256161,\n",
" -0.01029796, -0.01812954],\n",
" [ 0.04807831, -0.05462622, -0.06968277, ..., -0.07118641,\n",
" -0.03634901, -0.00619955],\n",
" [-0.02758568, 0.0021318 , 0.01136191, ..., -0.06439991,\n",
" -0.06434148, 0.01817922],\n",
" ...,\n",
" [ 0.02835793, -0.0673513 , 0.00254125, ..., -0.02448661,\n",
" -0.04257814, 0.03686985],\n",
" [ 0.07074963, -0.07194687, -0.06052938, ..., 0.02105471,\n",
" 0.05061468, 0.0740158 ],\n",
" [ 0.05856735, -0.00792463, 0.03314552, ..., 0.04626872,\n",
" 0.01818854, -0.0170098 ]], shape=(784, 300), dtype=float32)"
]
},
"execution_count": 27,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weights, biases = hidden1.get_weights()\n",
"weights"
]
},
{
"cell_type": "code",
"execution_count": 28,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(784, 300)"
]
},
"execution_count": 28,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"weights.shape"
]
},
{
"cell_type": "code",
"execution_count": 29,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.,\n",
" 0., 0., 0., 0., 0., 0., 0., 0., 0., 0., 0.], dtype=float32)"
]
},
"execution_count": 29,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"biases"
]
},
{
"cell_type": "code",
"execution_count": 30,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(300,)"
]
},
"execution_count": 30,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"biases.shape"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Compiling the model"
]
},
{
"cell_type": "code",
"execution_count": 31,
"metadata": {},
"outputs": [],
"source": [
"model.compile(loss=\"sparse_categorical_crossentropy\",\n",
" optimizer=\"sgd\",\n",
" metrics=[\"accuracy\"])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"This is equivalent to:"
]
},
{
"cell_type": "code",
"execution_count": 32,
"metadata": {},
"outputs": [],
"source": [
"# extra code – this cell is equivalent to the previous cell\n",
"model.compile(loss=tf.keras.losses.sparse_categorical_crossentropy,\n",
" optimizer=tf.keras.optimizers.SGD(),\n",
" metrics=[tf.keras.metrics.sparse_categorical_accuracy])"
]
},
{
"cell_type": "code",
"execution_count": 33,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
" [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]])"
]
},
"execution_count": 33,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# extra code – shows how to convert class ids to one-hot vectors\n",
"tf.keras.utils.to_categorical([0, 5, 1, 0], num_classes=10)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"Note: it's important to set `num_classes` when the number of classes is greater than the maximum class id in the sample."
]
},
{
"cell_type": "code",
"execution_count": 34,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([0, 5, 1, 0])"
]
},
"execution_count": 34,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"# extra code – shows how to convert one-hot vectors to class ids\n",
"np.argmax(\n",
" [[1., 0., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [0., 0., 0., 0., 0., 1., 0., 0., 0., 0.],\n",
" [0., 1., 0., 0., 0., 0., 0., 0., 0., 0.],\n",
" [1., 0., 0., 0., 0., 0., 0., 0., 0., 0.]],\n",
" axis=1\n",
")"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Training and evaluating the model"
]
},
{
"cell_type": "code",
"execution_count": 35,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Epoch 1/30\n"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"W0000 00:00:1774940213.361692 439194 cpu_allocator_impl.cc:82] Allocation of 172480000 exceeds 10% of free system memory.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.7012 - sparse_categorical_accuracy: 0.7671 - val_loss: 0.5043 - val_sparse_categorical_accuracy: 0.8250\n",
"Epoch 2/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.4883 - sparse_categorical_accuracy: 0.8299 - val_loss: 0.4526 - val_sparse_categorical_accuracy: 0.8362\n",
"Epoch 3/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.4440 - sparse_categorical_accuracy: 0.8452 - val_loss: 0.4284 - val_sparse_categorical_accuracy: 0.8478\n",
"Epoch 4/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.4167 - sparse_categorical_accuracy: 0.8544 - val_loss: 0.4121 - val_sparse_categorical_accuracy: 0.8536\n",
"Epoch 5/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.3969 - sparse_categorical_accuracy: 0.8608 - val_loss: 0.4021 - val_sparse_categorical_accuracy: 0.8546\n",
"Epoch 6/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.3811 - sparse_categorical_accuracy: 0.8661 - val_loss: 0.3945 - val_sparse_categorical_accuracy: 0.8574\n",
"Epoch 7/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.3679 - sparse_categorical_accuracy: 0.8705 - val_loss: 0.3860 - val_sparse_categorical_accuracy: 0.8582\n",
"Epoch 8/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.3564 - sparse_categorical_accuracy: 0.8745 - val_loss: 0.3800 - val_sparse_categorical_accuracy: 0.8618\n",
"Epoch 9/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.3460 - sparse_categorical_accuracy: 0.8779 - val_loss: 0.3754 - val_sparse_categorical_accuracy: 0.8630\n",
"Epoch 10/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.3366 - sparse_categorical_accuracy: 0.8801 - val_loss: 0.3711 - val_sparse_categorical_accuracy: 0.8642\n",
"Epoch 11/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.3279 - sparse_categorical_accuracy: 0.8829 - val_loss: 0.3664 - val_sparse_categorical_accuracy: 0.8658\n",
"Epoch 12/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.3199 - sparse_categorical_accuracy: 0.8857 - val_loss: 0.3630 - val_sparse_categorical_accuracy: 0.8672\n",
"Epoch 13/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.3123 - sparse_categorical_accuracy: 0.8878 - val_loss: 0.3610 - val_sparse_categorical_accuracy: 0.8680\n",
"Epoch 14/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.3054 - sparse_categorical_accuracy: 0.8902 - val_loss: 0.3575 - val_sparse_categorical_accuracy: 0.8692\n",
"Epoch 15/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.2988 - sparse_categorical_accuracy: 0.8926 - val_loss: 0.3541 - val_sparse_categorical_accuracy: 0.8698\n",
"Epoch 16/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.2925 - sparse_categorical_accuracy: 0.8953 - val_loss: 0.3528 - val_sparse_categorical_accuracy: 0.8706\n",
"Epoch 17/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.2866 - sparse_categorical_accuracy: 0.8976 - val_loss: 0.3517 - val_sparse_categorical_accuracy: 0.8706\n",
"Epoch 18/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2809 - sparse_categorical_accuracy: 0.8995 - val_loss: 0.3506 - val_sparse_categorical_accuracy: 0.8704\n",
"Epoch 19/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2755 - sparse_categorical_accuracy: 0.9012 - val_loss: 0.3500 - val_sparse_categorical_accuracy: 0.8726\n",
"Epoch 20/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.2703 - sparse_categorical_accuracy: 0.9034 - val_loss: 0.3500 - val_sparse_categorical_accuracy: 0.8734\n",
"Epoch 21/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 2ms/step - loss: 0.2653 - sparse_categorical_accuracy: 0.9050 - val_loss: 0.3484 - val_sparse_categorical_accuracy: 0.8732\n",
"Epoch 22/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.2604 - sparse_categorical_accuracy: 0.9071 - val_loss: 0.3483 - val_sparse_categorical_accuracy: 0.8732\n",
"Epoch 23/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.2558 - sparse_categorical_accuracy: 0.9087 - val_loss: 0.3482 - val_sparse_categorical_accuracy: 0.8742\n",
"Epoch 24/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.2513 - sparse_categorical_accuracy: 0.9104 - val_loss: 0.3483 - val_sparse_categorical_accuracy: 0.8740\n",
"Epoch 25/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.2470 - sparse_categorical_accuracy: 0.9121 - val_loss: 0.3479 - val_sparse_categorical_accuracy: 0.8750\n",
"Epoch 26/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.2427 - sparse_categorical_accuracy: 0.9137 - val_loss: 0.3470 - val_sparse_categorical_accuracy: 0.8746\n",
"Epoch 27/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.2386 - sparse_categorical_accuracy: 0.9153 - val_loss: 0.3469 - val_sparse_categorical_accuracy: 0.8748\n",
"Epoch 28/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.2346 - sparse_categorical_accuracy: 0.9170 - val_loss: 0.3470 - val_sparse_categorical_accuracy: 0.8758\n",
"Epoch 29/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.2306 - sparse_categorical_accuracy: 0.9189 - val_loss: 0.3470 - val_sparse_categorical_accuracy: 0.8760\n",
"Epoch 30/30\n",
"\u001b[1m1719/1719\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m4s\u001b[0m 2ms/step - loss: 0.2266 - sparse_categorical_accuracy: 0.9200 - val_loss: 0.3457 - val_sparse_categorical_accuracy: 0.8760\n"
]
}
],
"source": [
"history = model.fit(X_train, y_train, epochs=30,\n",
" validation_data=(X_valid, y_valid))"
]
},
{
"cell_type": "code",
"execution_count": 36,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'verbose': 'auto', 'epochs': 30, 'steps': 1719}"
]
},
"execution_count": 36,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"history.params"
]
},
{
"cell_type": "code",
"execution_count": 37,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29]\n"
]
}
],
"source": [
"print(history.epoch)"
]
},
{
"cell_type": "code",
"execution_count": 38,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
"text/plain": [
""
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"import matplotlib.pyplot as plt\n",
"import pandas as pd\n",
"\n",
"pd.DataFrame(history.history).plot(\n",
" figsize=(8, 5), xlim=[0, 29], ylim=[0, 1], grid=True, xlabel=\"Epoch\",\n",
" style=[\"r--\", \"r--.\", \"b-\", \"b-*\"])\n",
"plt.legend(loc=\"lower left\") # extra code\n",
"save_fig(\"keras_learning_curves_plot\") # extra code\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 39,
"metadata": {},
"outputs": [
{
"data": {
"image/png": 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",
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]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# extra code – shows how to shift the training curve by -1/2 epoch\n",
"plt.figure(figsize=(8, 5))\n",
"for key, style in zip(history.history, [\"r--\", \"r--.\", \"b-\", \"b-*\"]):\n",
" epochs = np.array(history.epoch) + (0 if key.startswith(\"val_\") else -0.5)\n",
" plt.plot(epochs, history.history[key], style, label=key)\n",
"plt.xlabel(\"Epoch\")\n",
"plt.axis([-0.5, 29, 0., 1])\n",
"plt.legend(loc=\"lower left\")\n",
"plt.grid()\n",
"plt.show()"
]
},
{
"cell_type": "code",
"execution_count": 40,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m109/313\u001b[0m \u001b[32m━━━━━━\u001b[0m\u001b[37m━━━━━━━━━━━━━━\u001b[0m \u001b[1m0s\u001b[0m 1ms/step - loss: 0.3542 - sparse_categorical_accuracy: 0.8762"
]
},
{
"name": "stderr",
"output_type": "stream",
"text": [
"W0000 00:00:1774940320.596303 439194 cpu_allocator_impl.cc:82] Allocation of 31360000 exceeds 10% of free system memory.\n"
]
},
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 2ms/step - loss: 0.3672 - sparse_categorical_accuracy: 0.8712\n"
]
},
{
"data": {
"text/plain": [
"[0.36717379093170166, 0.8712000250816345]"
]
},
"execution_count": 40,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"model.evaluate(X_test, y_test)"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"### Using the model to make predictions"
]
},
{
"cell_type": "code",
"execution_count": 41,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m1/1\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 50ms/step\n"
]
},
{
"data": {
"text/plain": [
"array([[0. , 0. , 0. , 0. , 0. , 0.3 , 0. , 0.01, 0. , 0.69],\n",
" [0. , 0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ],\n",
" [0. , 1. , 0. , 0. , 0. , 0. , 0. , 0. , 0. , 0. ]],\n",
" dtype=float32)"
]
},
"execution_count": 41,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"X_new = X_test[:3]\n",
"y_proba = model.predict(X_new)\n",
"y_proba.round(2)"
]
},
{
"cell_type": "code",
"execution_count": 42,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([9, 2, 1])"
]
},
"execution_count": 42,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"y_pred = y_proba.argmax(axis=-1)\n",
"y_pred"
]
},
{
"cell_type": "code",
"execution_count": 43,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array(['Ankle boot', 'Pullover', 'Trouser'], dtype='"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"# extra code – this cell generates and saves Figure 10–12\n",
"plt.figure(figsize=(7.2, 2.4))\n",
"for index, image in enumerate(X_new):\n",
" plt.subplot(1, 3, index + 1)\n",
" plt.imshow(image, cmap=\"binary\", interpolation=\"nearest\")\n",
" plt.axis('off')\n",
" plt.title(class_names[y_test[index]])\n",
"plt.subplots_adjust(wspace=0.2, hspace=0.5)\n",
"save_fig('fashion_mnist_images_plot', tight_layout=False)\n",
"plt.show()"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3 (ipykernel)",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.13.5"
},
"nav_menu": {
"height": "264px",
"width": "369px"
},
"toc": {
"navigate_menu": true,
"number_sections": true,
"sideBar": true,
"threshold": 6,
"toc_cell": false,
"toc_section_display": "block",
"toc_window_display": false
}
},
"nbformat": 4,
"nbformat_minor": 4
}
![\"Open](\"https://colab.research.google.com/assets/colab-badge.svg\"){kind=icon}
\n",
" ![](\"https://kaggle.com/static/images/open-in-kaggle.svg\")
\n",
"