Datasets:
HenryAI
/
KerasBERTv1-Data

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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),