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Signed-off-by: Jesse Chan <jesse.chan@databricks.com>
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jessechancy committed Dec 2, 2023
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140 changes: 117 additions & 23 deletions docs/source/deep-learning/keras/quickstart/quickstart_keras_core.ipynb
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"cell_type": "code",
"execution_count": 5,
"metadata": {},
"outputs": [],
"outputs": [
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1mModel: \"sequential\"\u001b[0m\n"
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"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
"┃<span style=\"font-weight: bold\"> Layer (type) </span>┃<span style=\"font-weight: bold\"> Output Shape </span>┃<span style=\"font-weight: bold\"> Param # </span>┃\n",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
"│ conv2d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">26</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">320</span> │\n",
"├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
"│ conv2d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">24</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
"├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
"│ conv2d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv2D</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">22</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">9,248</span> │\n",
"├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
"│ global_average_pooling2d │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │\n",
"│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">GlobalAveragePooling2D</span>) │ │ │\n",
"├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
"│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">10</span>) │ <span style=\"color: #00af00; text-decoration-color: #00af00\">330</span> │\n",
"└─────────────────────────────────┴───────────────────────────┴────────────┘\n",
"</pre>\n"
],
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"┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━┓\n",
"┃\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",
"┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━┩\n",
"│ conv2d (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m26\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m320\u001b[0m │\n",
"├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
"│ conv2d_1 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m24\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n",
"├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
"│ conv2d_2 (\u001b[38;5;33mConv2D\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m22\u001b[0m, \u001b[38;5;34m22\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m9,248\u001b[0m │\n",
"├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
"│ global_average_pooling2d │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m) │ \u001b[38;5;34m0\u001b[0m │\n",
"│ (\u001b[38;5;33mGlobalAveragePooling2D\u001b[0m) │ │ │\n",
"├─────────────────────────────────┼───────────────────────────┼────────────┤\n",
"│ dense (\u001b[38;5;33mDense\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m10\u001b[0m) │ \u001b[38;5;34m330\u001b[0m │\n",
"└─────────────────────────────────┴───────────────────────────┴────────────┘\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">19,146</span> (74.79 KB)\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1m Total params: \u001b[0m\u001b[38;5;34m19,146\u001b[0m (74.79 KB)\n"
]
},
"metadata": {},
"output_type": "display_data"
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{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">19,146</span> (74.79 KB)\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m19,146\u001b[0m (74.79 KB)\n"
]
},
"metadata": {},
"output_type": "display_data"
},
{
"data": {
"text/html": [
"<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
"</pre>\n"
],
"text/plain": [
"\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"NUM_CLASSES = 10\n",
"INPUT_SHAPE = (28, 28, 1)\n",
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" return model\n",
"\n",
"\n",
"model = initialize_model()"
"model = initialize_model()\n",
"model.summary()"
]
},
{
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"output_type": "stream",
"text": [
"Epoch 1/3\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 34ms/step - accuracy: 0.5446 - loss: 1.4942 - val_accuracy: 0.9325 - val_loss: 0.2448\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 34ms/step - accuracy: 0.5922 - loss: 1.2862 - val_accuracy: 0.9427 - val_loss: 0.2075\n",
"Epoch 2/3\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 33ms/step - accuracy: 0.9253 - loss: 0.2524 - val_accuracy: 0.9390 - val_loss: 0.1956\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 33ms/step - accuracy: 0.9330 - loss: 0.2286 - val_accuracy: 0.9348 - val_loss: 0.2020\n",
"Epoch 3/3\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 35ms/step - accuracy: 0.9437 - loss: 0.1850 - val_accuracy: 0.9640 - val_loss: 0.1236\n"
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 33ms/step - accuracy: 0.9499 - loss: 0.1671 - val_accuracy: 0.9558 - val_loss: 0.1491\n"
]
}
],
Expand All @@ -207,15 +300,16 @@
" metrics=[\"accuracy\"],\n",
")\n",
"\n",
"with mlflow.start_run() as run:\n",
" history = model.fit(\n",
" x_train,\n",
" y_train,\n",
" batch_size=BATCH_SIZE,\n",
" epochs=EPOCHS,\n",
" validation_split=0.1,\n",
" callbacks=[mlflow.keras_core.MLflowCallback(run)],\n",
" )\n"
"run = mlflow.start_run()\n",
"model.fit(\n",
" x_train,\n",
" y_train,\n",
" batch_size=BATCH_SIZE,\n",
" epochs=EPOCHS,\n",
" validation_split=0.1,\n",
" callbacks=[mlflow.keras_core.MLflowCallback(run)],\n",
")\n",
"mlflow.end_run()"
]
},
{
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"output_type": "stream",
"text": [
"Epoch 1/3\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 34ms/step - accuracy: 0.4919 - loss: 1.6902 - val_accuracy: 0.9182 - val_loss: 0.2956\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m30s\u001b[0m 34ms/step - accuracy: 0.6151 - loss: 1.2100 - val_accuracy: 0.9373 - val_loss: 0.2144\n",
"Epoch 2/3\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 34ms/step - accuracy: 0.9245 - loss: 0.2641 - val_accuracy: 0.9453 - val_loss: 0.1888\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 34ms/step - accuracy: 0.9274 - loss: 0.2459 - val_accuracy: 0.9608 - val_loss: 0.1338\n",
"Epoch 3/3\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 34ms/step - accuracy: 0.9424 - loss: 0.1912 - val_accuracy: 0.9595 - val_loss: 0.1486\n"
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 34ms/step - accuracy: 0.9477 - loss: 0.1738 - val_accuracy: 0.9577 - val_loss: 0.1454\n"
]
}
],
Expand All @@ -273,7 +367,7 @@
" callbacks=[\n",
" mlflow.keras_core.MLflowCallback(run, log_every_epoch=False, log_every_n_steps=5)\n",
" ],\n",
" )\n"
" )"
]
},
{
Expand Down Expand Up @@ -342,11 +436,11 @@
"output_type": "stream",
"text": [
"Epoch 1/3\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 34ms/step - accuracy: 0.5406 - loss: 1.5045 - val_accuracy: 0.9255 - val_loss: 0.2602\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 34ms/step - accuracy: 0.5645 - loss: 1.4105 - val_accuracy: 0.9187 - val_loss: 0.2826\n",
"Epoch 2/3\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m28s\u001b[0m 34ms/step - accuracy: 0.9159 - loss: 0.2823 - val_accuracy: 0.9460 - val_loss: 0.1794\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 34ms/step - accuracy: 0.9257 - loss: 0.2615 - val_accuracy: 0.9602 - val_loss: 0.1368\n",
"Epoch 3/3\n",
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 34ms/step - accuracy: 0.9450 - loss: 0.1870 - val_accuracy: 0.9540 - val_loss: 0.1510\n"
"\u001b[1m844/844\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m29s\u001b[0m 34ms/step - accuracy: 0.9456 - loss: 0.1800 - val_accuracy: 0.9678 - val_loss: 0.1037\n"
]
}
],
Expand All @@ -367,7 +461,7 @@
" epochs=EPOCHS,\n",
" validation_split=0.1,\n",
" callbacks=[MlflowCallbackLogPerBatch(run, log_every_epoch=False, log_every_n_steps=5)],\n",
" )\n"
" )"
]
},
{
Expand Down Expand Up @@ -398,7 +492,7 @@
"name": "stdout",
"output_type": "stream",
"text": [
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9402 - loss: 0.1886\n"
"\u001b[1m313/313\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m1s\u001b[0m 4ms/step - accuracy: 0.9541 - loss: 0.1487\n"
]
}
],
Expand Down

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