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decoder_input = keras.Input(shape=(16,), name="encoded_img") |
x = layers.Reshape((4, 4, 1))(decoder_input) |
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) |
decoder = keras.Model(decoder_input, decoder_output, name="decoder") |
decoder.summary() |
autoencoder_input = keras.Input(shape=(28, 28, 1), name="img") |
encoded_img = encoder(autoencoder_input) |
decoded_img = decoder(encoded_img) |
autoencoder = keras.Model(autoencoder_input, decoded_img, name="autoencoder") |
autoencoder.summary() |
Model: "encoder" |
_________________________________________________________________ |
Layer (type) Output Shape Param # |
================================================================= |
original_img (InputLayer) [(None, 28, 28, 1)] 0 |
_________________________________________________________________ |
conv2d_4 (Conv2D) (None, 26, 26, 16) 160 |
_________________________________________________________________ |
conv2d_5 (Conv2D) (None, 24, 24, 32) 4640 |
_________________________________________________________________ |
max_pooling2d_1 (MaxPooling2 (None, 8, 8, 32) 0 |
_________________________________________________________________ |
conv2d_6 (Conv2D) (None, 6, 6, 32) 9248 |
_________________________________________________________________ |
conv2d_7 (Conv2D) (None, 4, 4, 16) 4624 |
_________________________________________________________________ |
global_max_pooling2d_1 (Glob (None, 16) 0 |
================================================================= |
Total params: 18,672 |
Trainable params: 18,672 |
Non-trainable params: 0 |
_________________________________________________________________ |
Model: "decoder" |
_________________________________________________________________ |
Layer (type) Output Shape Param # |
================================================================= |
encoded_img (InputLayer) [(None, 16)] 0 |
_________________________________________________________________ |
reshape_1 (Reshape) (None, 4, 4, 1) 0 |
_________________________________________________________________ |
conv2d_transpose_4 (Conv2DTr (None, 6, 6, 16) 160 |
_________________________________________________________________ |
conv2d_transpose_5 (Conv2DTr (None, 8, 8, 32) 4640 |
_________________________________________________________________ |
up_sampling2d_1 (UpSampling2 (None, 24, 24, 32) 0 |
_________________________________________________________________ |
conv2d_transpose_6 (Conv2DTr (None, 26, 26, 16) 4624 |
_________________________________________________________________ |
conv2d_transpose_7 (Conv2DTr (None, 28, 28, 1) 145 |
================================================================= |
Total params: 9,569 |
Trainable params: 9,569 |
Non-trainable params: 0 |
_________________________________________________________________ |
Model: "autoencoder" |
_________________________________________________________________ |
Layer (type) Output Shape Param # |
================================================================= |
img (InputLayer) [(None, 28, 28, 1)] 0 |
_________________________________________________________________ |
encoder (Functional) (None, 16) 18672 |
_________________________________________________________________ |
decoder (Functional) (None, 28, 28, 1) 9569 |
================================================================= |
Total params: 28,241 |
Trainable params: 28,241 |
Non-trainable params: 0 |
_________________________________________________________________ |
As you can see, the model can be nested: a model can contain sub-models (since a model is just like a layer). A common use case for model nesting is ensembling. For example, here's how to ensemble a set of models into a single model that averages their predictions: |
def get_model(): |
inputs = keras.Input(shape=(128,)) |
outputs = layers.Dense(1)(inputs) |
return keras.Model(inputs, outputs) |
model1 = get_model() |
model2 = get_model() |
model3 = get_model() |
inputs = keras.Input(shape=(128,)) |
y1 = model1(inputs) |
y2 = model2(inputs) |
y3 = model3(inputs) |
outputs = layers.average([y1, y2, y3]) |
ensemble_model = keras.Model(inputs=inputs, outputs=outputs) |
Manipulate complex graph topologies |
Models with multiple inputs and outputs |
The functional API makes it easy to manipulate multiple inputs and outputs. This cannot be handled with the Sequential API. |
For example, if you're building a system for ranking customer issue tickets by priority and routing them to the correct department, then the model will have three inputs: |
the title of the ticket (text input), |
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