def preprocess(x):
"""Cast to float, normalize, and concatenate images along last axis."""
x = nest.map_structure(
lambda image: tf.image.convert_image_dtype(image, tf.float32), x)
x = nest.flatten(x)
x = tf.concat(x, axis=-1)
x = (tf.image.convert_image_dtype(x, tf.float32) - 0.5) * 2.0
return x
def preprocess(x):
"""Cast to float, normalize, and concatenate images along last axis."""
import tensorflow as tf
x = nest.map_structure(
lambda image: tf.image.convert_image_dtype(image, tf.float32), x)
x = nest.flatten(x)
x = tf.concat(x, axis=-1)
# x = (tf.image.convert_image_dtype(x, tf.float32) - 0.5) * 2.0
# TODO: Why is the image being converted to float32 twice? Once in the
# nest and once down here?
x = (high - low) * tf.image.convert_image_dtype(x, tf.float32) + low
return x
def feedforward_model(hidden_layer_sizes,
output_size,
activation='relu',
output_activation='linear',
preprocessors=None,
dropout=None,
name='feedforward_model',
*args,
**kwargs):
def cast_and_concat(x):
x = nest.map_structure(training_utils.cast_if_floating_dtype, x)
x = nest.flatten(x)
x = tf.concat(x, axis=-1)
return x
if dropout:
layers = nest.flatten([[
tf.keras.layers.Dense(hidden_layer_size,
*args,
activation=activation,
**kwargs),
tf.keras.layers.Dropout(dropout)
] for hidden_layer_size in hidden_layer_sizes])
else:
layers = [
tf.keras.layers.Dense(hidden_layer_size,
*args,
activation=activation,
**kwargs)
for hidden_layer_size in hidden_layer_sizes
]
model = PicklableSequential(
(tfkl.Lambda(cast_and_concat), *layers,
tf.keras.layers.Dense(
output_size, *args, activation=output_activation, **kwargs)),
name=name)
return model
def cast_and_concat(x):
x = nest.map_structure(lambda element: tf.cast(element, tf.float32), x)
x = nest.flatten(x)
x = tf.concat(x, axis=-1)
return x
def flatten_input_structure(inputs):
inputs_flat = nest.flatten(inputs)
return inputs_flat
def cast_and_concat(x):
x = nest.map_structure(training_utils.cast_if_floating_dtype, x)
x = nest.flatten(x)
x = tf.concat(x, axis=-1)
return x