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Use the same graph of layers to define multiple models |
In the functional API, models are created by specifying their inputs and outputs in a graph of layers. That means that a single graph of layers can be used to generate multiple models. |
In the example below, you use the same stack of layers to instantiate two models: an encoder model that turns image inputs into 16-dimensional vectors, and an end-to-end autoencoder model for training. |
encoder_input = keras.Input(shape=(28, 28, 1), name="img") |
x = layers.Conv2D(16, 3, activation="relu")(encoder_input) |
x = layers.Conv2D(32, 3, activation="relu")(x) |
x = layers.MaxPooling2D(3)(x) |
x = layers.Conv2D(32, 3, activation="relu")(x) |
x = layers.Conv2D(16, 3, activation="relu")(x) |
encoder_output = layers.GlobalMaxPooling2D()(x) |
encoder = keras.Model(encoder_input, encoder_output, name="encoder") |
encoder.summary() |
x = layers.Reshape((4, 4, 1))(encoder_output) |
x = layers.Conv2DTranspose(16, 3, activation="relu")(x) |
x = layers.Conv2DTranspose(32, 3, activation="relu")(x) |
x = layers.UpSampling2D(3)(x) |
x = layers.Conv2DTranspose(16, 3, activation="relu")(x) |
decoder_output = layers.Conv2DTranspose(1, 3, activation="relu")(x) |
autoencoder = keras.Model(encoder_input, decoder_output, name="autoencoder") |
autoencoder.summary() |
Model: "encoder" |
_________________________________________________________________ |
Layer (type) Output Shape Param # |
================================================================= |
img (InputLayer) [(None, 28, 28, 1)] 0 |
_________________________________________________________________ |
conv2d (Conv2D) (None, 26, 26, 16) 160 |
_________________________________________________________________ |
conv2d_1 (Conv2D) (None, 24, 24, 32) 4640 |
_________________________________________________________________ |
max_pooling2d (MaxPooling2D) (None, 8, 8, 32) 0 |
_________________________________________________________________ |
conv2d_2 (Conv2D) (None, 6, 6, 32) 9248 |
_________________________________________________________________ |
conv2d_3 (Conv2D) (None, 4, 4, 16) 4624 |
_________________________________________________________________ |
global_max_pooling2d (Global (None, 16) 0 |
================================================================= |
Total params: 18,672 |
Trainable params: 18,672 |
Non-trainable params: 0 |
_________________________________________________________________ |
Model: "autoencoder" |
_________________________________________________________________ |
Layer (type) Output Shape Param # |
================================================================= |
img (InputLayer) [(None, 28, 28, 1)] 0 |
_________________________________________________________________ |
conv2d (Conv2D) (None, 26, 26, 16) 160 |
_________________________________________________________________ |
conv2d_1 (Conv2D) (None, 24, 24, 32) 4640 |
_________________________________________________________________ |
max_pooling2d (MaxPooling2D) (None, 8, 8, 32) 0 |
_________________________________________________________________ |
conv2d_2 (Conv2D) (None, 6, 6, 32) 9248 |
_________________________________________________________________ |
conv2d_3 (Conv2D) (None, 4, 4, 16) 4624 |
_________________________________________________________________ |
global_max_pooling2d (Global (None, 16) 0 |
_________________________________________________________________ |
reshape (Reshape) (None, 4, 4, 1) 0 |
_________________________________________________________________ |
conv2d_transpose (Conv2DTran (None, 6, 6, 16) 160 |
_________________________________________________________________ |
conv2d_transpose_1 (Conv2DTr (None, 8, 8, 32) 4640 |
_________________________________________________________________ |
up_sampling2d (UpSampling2D) (None, 24, 24, 32) 0 |
_________________________________________________________________ |
conv2d_transpose_2 (Conv2DTr (None, 26, 26, 16) 4624 |
_________________________________________________________________ |
conv2d_transpose_3 (Conv2DTr (None, 28, 28, 1) 145 |
================================================================= |
Total params: 28,241 |
Trainable params: 28,241 |
Non-trainable params: 0 |
_________________________________________________________________ |
Here, the decoding architecture is strictly symmetrical to the encoding architecture, so the output shape is the same as the input shape (28, 28, 1). |
The reverse of a Conv2D layer is a Conv2DTranspose layer, and the reverse of a MaxPooling2D layer is an UpSampling2D layer. |
All models are callable, just like layers |
You can treat any model as if it were a layer by invoking it on an Input or on the output of another layer. By calling a model you aren't just reusing the architecture of the model, you're also reusing its weights. |
To see this in action, here's a different take on the autoencoder example that creates an encoder model, a decoder model, and chains them in two calls to obtain the autoencoder model: |
encoder_input = keras.Input(shape=(28, 28, 1), name="original_img") |
x = layers.Conv2D(16, 3, activation="relu")(encoder_input) |
x = layers.Conv2D(32, 3, activation="relu")(x) |
x = layers.MaxPooling2D(3)(x) |
x = layers.Conv2D(32, 3, activation="relu")(x) |
x = layers.Conv2D(16, 3, activation="relu")(x) |
encoder_output = layers.GlobalMaxPooling2D()(x) |
encoder = keras.Model(encoder_input, encoder_output, name="encoder") |
encoder.summary() |
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