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x_train = np.expand_dims(x_train, -1) |
x_test = np.expand_dims(x_test, -1) |
print(\"x_train shape:\", x_train.shape) |
print(x_train.shape[0], \"train samples\") |
print(x_test.shape[0], \"test samples\") |
# Convert class vectors to binary class matrices. |
y_train = keras.utils.to_categorical(y_train, num_classes) |
y_test = keras.utils.to_categorical(y_test, num_classes) |
model = keras.Sequential( |
[ |
keras.Input(shape=input_shape), |
layers.Conv2D(32, kernel_size=(3, 3), activation=\"relu\"), |
layers.MaxPooling2D(pool_size=(2, 2)), |
layers.Conv2D(64, kernel_size=(3, 3), activation=\"relu\"), |
layers.MaxPooling2D(pool_size=(2, 2)), |
layers.Flatten(), |
layers.Dropout(0.5), |
layers.Dense(num_classes, activation=\"softmax\"), |
] |
) |
model.summary() |
batch_size = 128 |
epochs = 15 |
model.compile(loss=\"categorical_crossentropy\", optimizer=\"adam\", metrics=[\"accuracy\"]) |
callbacks = [JaccardScoreCallback(model, x_test, np.argmax(y_test, axis=-1), \"logs\")] |
model.fit( |
x_train, |
y_train, |
batch_size=batch_size, |
epochs=epochs, |
validation_split=0.1, |
callbacks=callbacks, |
) |
x_train shape: (60000, 28, 28, 1) |
60000 train samples |
10000 test samples |
Model: \"sequential\" |
_________________________________________________________________ |
Layer (type) Output Shape Param # |
================================================================= |
conv2d (Conv2D) (None, 26, 26, 32) 320 |
_________________________________________________________________ |
max_pooling2d (MaxPooling2D) (None, 13, 13, 32) 0 |
_________________________________________________________________ |
conv2d_1 (Conv2D) (None, 11, 11, 64) 18496 |
_________________________________________________________________ |
max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64) 0 |
_________________________________________________________________ |
flatten (Flatten) (None, 1600) 0 |
_________________________________________________________________ |
dropout (Dropout) (None, 1600) 0 |
_________________________________________________________________ |
dense (Dense) (None, 10) 16010 |
================================================================= |
Total params: 34,826 |
Trainable params: 34,826 |
Non-trainable params: 0 |
_________________________________________________________________ |
Epoch 1/15 |
422/422 [==============================] - 6s 14ms/step - loss: 0.3661 - accuracy: 0.8895 - val_loss: 0.0823 - val_accuracy: 0.9765 |
Epoch 2/15 |
422/422 [==============================] - 6s 14ms/step - loss: 0.1119 - accuracy: 0.9653 - val_loss: 0.0620 - val_accuracy: 0.9823 |
Epoch 3/15 |
422/422 [==============================] - 6s 14ms/step - loss: 0.0841 - accuracy: 0.9742 - val_loss: 0.0488 - val_accuracy: 0.9873 |
Epoch 4/15 |
422/422 [==============================] - 6s 14ms/step - loss: 0.0696 - accuracy: 0.9787 - val_loss: 0.0404 - val_accuracy: 0.9888 |
Epoch 5/15 |
422/422 [==============================] - 6s 14ms/step - loss: 0.0615 - accuracy: 0.9813 - val_loss: 0.0406 - val_accuracy: 0.9897 |
Epoch 6/15 |
422/422 [==============================] - 6s 13ms/step - loss: 0.0565 - accuracy: 0.9826 - val_loss: 0.0373 - val_accuracy: 0.9900 |
Epoch 7/15 |
422/422 [==============================] - 6s 14ms/step - loss: 0.0520 - accuracy: 0.9833 - val_loss: 0.0369 - val_accuracy: 0.9898 |
Epoch 8/15 |
422/422 [==============================] - 6s 14ms/step - loss: 0.0488 - accuracy: 0.9851 - val_loss: 0.0353 - val_accuracy: 0.9905 |
Epoch 9/15 |
422/422 [==============================] - 6s 14ms/step - loss: 0.0440 - accuracy: 0.9861 - val_loss: 0.0347 - val_accuracy: 0.9893 |
Epoch 10/15 |
422/422 [==============================] - 6s 14ms/step - loss: 0.0424 - accuracy: 0.9871 - val_loss: 0.0294 - val_accuracy: 0.9907 |
Epoch 11/15 |
422/422 [==============================] - 6s 14ms/step - loss: 0.0402 - accuracy: 0.9874 - val_loss: 0.0340 - val_accuracy: 0.9903 |
Epoch 12/15 |
422/422 [==============================] - 6s 13ms/step - loss: 0.0382 - accuracy: 0.9878 - val_loss: 0.0290 - val_accuracy: 0.9917 |
Epoch 13/15 |
422/422 [==============================] - 6s 14ms/step - loss: 0.0358 - accuracy: 0.9886 - val_loss: 0.0286 - val_accuracy: 0.9923 |
Epoch 14/15 |
422/422 [==============================] - 6s 13ms/step - loss: 0.0349 - accuracy: 0.9885 - val_loss: 0.0282 - val_accuracy: 0.9918 |
Epoch 15/15 |
422/422 [==============================] - 6s 14ms/step - loss: 0.0323 - accuracy: 0.9899 - val_loss: 0.0283 - val_accuracy: 0.9922 |
<tensorflow.python.keras.callbacks.History at 0x7f62fc5786d0> |
If you now launch a TensorBoard instance using tensorboard --logdir=logs, you will see the jaccard_score metric alongside any other exported metrics! |
TensorBoard Jaccard Score |
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