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# Just for demonstration purposes. |
img_inputs = keras.Input(shape=(32, 32, 3)) |
The inputs that is returned contains information about the shape and dtype of the input data that you feed to your model. Here's the shape: |
inputs.shape |
TensorShape([None, 784]) |
Here's the dtype: |
inputs.dtype |
tf.float32 |
You create a new node in the graph of layers by calling a layer on this inputs object: |
dense = layers.Dense(64, activation="relu") |
x = dense(inputs) |
The "layer call" action is like drawing an arrow from "inputs" to this layer you created. You're "passing" the inputs to the dense layer, and you get x as the output. |
Let's add a few more layers to the graph of layers: |
x = layers.Dense(64, activation="relu")(x) |
outputs = layers.Dense(10)(x) |
At this point, you can create a Model by specifying its inputs and outputs in the graph of layers: |
model = keras.Model(inputs=inputs, outputs=outputs, name="mnist_model") |
Let's check out what the model summary looks like: |
model.summary() |
Model: "mnist_model" |
_________________________________________________________________ |
Layer (type) Output Shape Param # |
================================================================= |
input_1 (InputLayer) [(None, 784)] 0 |
_________________________________________________________________ |
dense (Dense) (None, 64) 50240 |
_________________________________________________________________ |
dense_1 (Dense) (None, 64) 4160 |
_________________________________________________________________ |
dense_2 (Dense) (None, 10) 650 |
================================================================= |
Total params: 55,050 |
Trainable params: 55,050 |
Non-trainable params: 0 |
_________________________________________________________________ |
You can also plot the model as a graph: |
keras.utils.plot_model(model, "my_first_model.png") |
png |
And, optionally, display the input and output shapes of each layer in the plotted graph: |
keras.utils.plot_model(model, "my_first_model_with_shape_info.png", show_shapes=True) |
png |
This figure and the code are almost identical. In the code version, the connection arrows are replaced by the call operation. |
A "graph of layers" is an intuitive mental image for a deep learning model, and the functional API is a way to create models that closely mirrors this. |
Training, evaluation, and inference |
Training, evaluation, and inference work exactly in the same way for models built using the functional API as for Sequential models. |
The Model class offers a built-in training loop (the fit() method) and a built-in evaluation loop (the evaluate() method). Note that you can easily customize these loops to implement training routines beyond supervised learning (e.g. GANs). |
Here, load the MNIST image data, reshape it into vectors, fit the model on the data (while monitoring performance on a validation split), then evaluate the model on the test data: |
(x_train, y_train), (x_test, y_test) = keras.datasets.mnist.load_data() |
x_train = x_train.reshape(60000, 784).astype("float32") / 255 |
x_test = x_test.reshape(10000, 784).astype("float32") / 255 |
model.compile( |
loss=keras.losses.SparseCategoricalCrossentropy(from_logits=True), |
optimizer=keras.optimizers.RMSprop(), |
metrics=["accuracy"], |
) |
history = model.fit(x_train, y_train, batch_size=64, epochs=2, validation_split=0.2) |
test_scores = model.evaluate(x_test, y_test, verbose=2) |
print("Test loss:", test_scores[0]) |
print("Test accuracy:", test_scores[1]) |
Epoch 1/2 |
750/750 [==============================] - 2s 2ms/step - loss: 0.5648 - accuracy: 0.8473 - val_loss: 0.1793 - val_accuracy: 0.9474 |
Epoch 2/2 |
750/750 [==============================] - 1s 1ms/step - loss: 0.1686 - accuracy: 0.9506 - val_loss: 0.1398 - val_accuracy: 0.9576 |
313/313 - 0s - loss: 0.1401 - accuracy: 0.9580 |
Test loss: 0.14005452394485474 |
Test accuracy: 0.9580000042915344 |
For further reading, see the training and evaluation guide. |
Save and serialize |
Saving the model and serialization work the same way for models built using the functional API as they do for Sequential models. The standard way to save a functional model is to call model.save() to save the entire model as a single file. You can later recreate the same model from this file, even if the code that built the model is no longer available. |
This saved file includes the: - model architecture - model weight values (that were learned during training) - model training config, if any (as passed to compile) - optimizer and its state, if any (to restart training where you left off) |
model.save("path_to_my_model") |
del model |
# Recreate the exact same model purely from the file: |
model = keras.models.load_model("path_to_my_model") |
INFO:tensorflow:Assets written to: path_to_my_model/assets |
For details, read the model serialization & saving guide. |
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