def build_and_compile_model(train_features):
normalizer = layers.Normalization(axis=-1)
normalizer.adapt(np.array(train_features))
model = keras.Sequential(
[layers.Dense(100, activation='relu'),
layers.Dense(1)])
optimizer = tf.keras.optimizers.RMSprop(0.001)
model.compile(loss='mse', optimizer=optimizer, metrics=['mae', 'mse'])
return model
projection_dim = 64
num_heads = 4
transformer_units = [
projection_dim * 2,
projection_dim,
] # Size of the transformer layers
transformer_layers = 8
# Size of the dense layers of the final classifier
mlp_head_units = [2048, 1024]
"""
## Use data augmentation
"""
data_augmentation = keras.Sequential(
[
layers.Normalization(),
layers.Resizing(image_size, image_size),
layers.RandomFlip("horizontal"),
layers.RandomRotation(factor=0.02),
layers.RandomZoom(height_factor=0.2, width_factor=0.2),
],
name="data_augmentation",
)
# Compute the mean and the variance of the training data for normalization.
data_augmentation.layers[0].adapt(x_train)
"""
## Implement multilayer perceptron (MLP)
"""
def mlp(x, hidden_units, dropout_rate):
You set the state of a preprocessing layer by exposing it to training data, via the
`adapt()` method:
"""
import numpy as np
import tensorflow as tf
from tensorflow.keras import layers
data = np.array([
[0.1, 0.2, 0.3],
[0.8, 0.9, 1.0],
[1.5, 1.6, 1.7],
])
layer = layers.Normalization()
layer.adapt(data)
normalized_data = layer(data)
print("Features mean: %.2f" % (normalized_data.numpy().mean()))
print("Features std: %.2f" % (normalized_data.numpy().std()))
"""
The `adapt()` method takes either a Numpy array or a `tf.data.Dataset` object. In the
case of `StringLookup` and `TextVectorization`, you can also pass a list of strings:
"""
data = [
"ξεῖν᾽, ἦ τοι μὲν ὄνειροι ἀμήχανοι ἀκριτόμυθοι",
"γίγνοντ᾽, οὐδέ τι πάντα τελείεται ἀνθρώποισι.",
"δοιαὶ γάρ τε πύλαι ἀμενηνῶν εἰσὶν ὀνείρων:",
"αἱ μὲν γὰρ κεράεσσι τετεύχαται, αἱ δ᾽ ἐλέφαντι:",
def build(self, hp, inputs=None):
input_node = nest.flatten(inputs)[0]
return layers.Normalization(axis=self.axis)(input_node)
test_ds = preprocess_dataset(test_files)
batch_size = 64
train_ds = train_ds.batch(batch_size)
val_ds = val_ds.batch(batch_size)
train_ds = train_ds.cache().prefetch(AUTOTUNE)
val_ds = val_ds.cache().prefetch(AUTOTUNE)
for spectrogram, _ in spectrogram_ds.take(1):
input_shape = spectrogram.shape
print('Input shape:', input_shape)
num_labels = len(commands)
# Instantiate the `tf.keras.layers.Normalization` layer.
norm_layer = layers.Normalization()
# Fit the state of the layer to the spectrograms
# with `Normalization.adapt`.
norm_layer.adapt(data=spectrogram_ds.map(map_func=lambda spec, label: spec))
model = models.Sequential([
layers.Input(shape=input_shape),
# Downsample the input.
layers.Resizing(32, 32),
# Normalize.
norm_layer,
layers.Conv2D(32, 3, activation='relu'),
layers.Conv2D(64, 3, activation='relu'),
layers.MaxPooling2D(),
layers.Dropout(0.25),
layers.Flatten(),