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inputs = keras.Input(shape=(32, 32, 3), name="img") |
x = layers.Conv2D(32, 3, activation="relu")(inputs) |
x = layers.Conv2D(64, 3, activation="relu")(x) |
block_1_output = layers.MaxPooling2D(3)(x) |
x = layers.Conv2D(64, 3, activation="relu", padding="same")(block_1_output) |
x = layers.Conv2D(64, 3, activation="relu", padding="same")(x) |
block_2_output = layers.add([x, block_1_output]) |
x = layers.Conv2D(64, 3, activation="relu", padding="same")(block_2_output) |
x = layers.Conv2D(64, 3, activation="relu", padding="same")(x) |
block_3_output = layers.add([x, block_2_output]) |
x = layers.Conv2D(64, 3, activation="relu")(block_3_output) |
x = layers.GlobalAveragePooling2D()(x) |
x = layers.Dense(256, activation="relu")(x) |
x = layers.Dropout(0.5)(x) |
outputs = layers.Dense(10)(x) |
model = keras.Model(inputs, outputs, name="toy_resnet") |
model.summary() |
Model: "toy_resnet" |
__________________________________________________________________________________________________ |
Layer (type) Output Shape Param # Connected to |
================================================================================================== |
img (InputLayer) [(None, 32, 32, 3)] 0 |
__________________________________________________________________________________________________ |
conv2d_8 (Conv2D) (None, 30, 30, 32) 896 img[0][0] |
__________________________________________________________________________________________________ |
conv2d_9 (Conv2D) (None, 28, 28, 64) 18496 conv2d_8[0][0] |
__________________________________________________________________________________________________ |
max_pooling2d_2 (MaxPooling2D) (None, 9, 9, 64) 0 conv2d_9[0][0] |
__________________________________________________________________________________________________ |
conv2d_10 (Conv2D) (None, 9, 9, 64) 36928 max_pooling2d_2[0][0] |
__________________________________________________________________________________________________ |
conv2d_11 (Conv2D) (None, 9, 9, 64) 36928 conv2d_10[0][0] |
__________________________________________________________________________________________________ |
add (Add) (None, 9, 9, 64) 0 conv2d_11[0][0] |
max_pooling2d_2[0][0] |
__________________________________________________________________________________________________ |
conv2d_12 (Conv2D) (None, 9, 9, 64) 36928 add[0][0] |
__________________________________________________________________________________________________ |
conv2d_13 (Conv2D) (None, 9, 9, 64) 36928 conv2d_12[0][0] |
__________________________________________________________________________________________________ |
add_1 (Add) (None, 9, 9, 64) 0 conv2d_13[0][0] |
add[0][0] |
__________________________________________________________________________________________________ |
conv2d_14 (Conv2D) (None, 7, 7, 64) 36928 add_1[0][0] |
__________________________________________________________________________________________________ |
global_average_pooling2d (Globa (None, 64) 0 conv2d_14[0][0] |
__________________________________________________________________________________________________ |
dense_6 (Dense) (None, 256) 16640 global_average_pooling2d[0][0] |
__________________________________________________________________________________________________ |
dropout (Dropout) (None, 256) 0 dense_6[0][0] |
__________________________________________________________________________________________________ |
dense_7 (Dense) (None, 10) 2570 dropout[0][0] |
================================================================================================== |
Total params: 223,242 |
Trainable params: 223,242 |
Non-trainable params: 0 |
__________________________________________________________________________________________________ |
Plot the model: |
keras.utils.plot_model(model, "mini_resnet.png", show_shapes=True) |
png |
Now train the model: |
(x_train, y_train), (x_test, y_test) = keras.datasets.cifar10.load_data() |
x_train = x_train.astype("float32") / 255.0 |
x_test = x_test.astype("float32") / 255.0 |
y_train = keras.utils.to_categorical(y_train, 10) |
y_test = keras.utils.to_categorical(y_test, 10) |
model.compile( |
optimizer=keras.optimizers.RMSprop(1e-3), |
loss=keras.losses.CategoricalCrossentropy(from_logits=True), |
metrics=["acc"], |
) |
# We restrict the data to the first 1000 samples so as to limit execution time |
# on Colab. Try to train on the entire dataset until convergence! |
model.fit(x_train[:1000], y_train[:1000], batch_size=64, epochs=1, validation_split=0.2) |
13/13 [==============================] - 2s 103ms/step - loss: 2.3218 - acc: 0.1291 - val_loss: 2.3014 - val_acc: 0.1150 |
<tensorflow.python.keras.callbacks.History at 0x157848990> |
Shared layers |
Another good use for the functional API are models that use shared layers. Shared layers are layer instances that are reused multiple times in the same model -- they learn features that correspond to multiple paths in the graph-of-layers. |
Shared layers are often used to encode inputs from similar spaces (say, two different pieces of text that feature similar vocabulary). They enable sharing of information across these different inputs, and they make it possible to train such a model on less data. If a given word is seen in one of the inputs, that will benefit the processing of all inputs that pass through the shared layer. |
To share a layer in the functional API, call the same layer instance multiple times. For instance, here's an Embedding layer shared across two different text inputs: |
# Embedding for 1000 unique words mapped to 128-dimensional vectors |
shared_embedding = layers.Embedding(1000, 128) |
# Variable-length sequence of integers |
text_input_a = keras.Input(shape=(None,), dtype="int32") |
# Variable-length sequence of integers |
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