Module keras.distribute.simple_models
A simple functional keras model with one layer.
Expand source code
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
# ==============================================================================
"""A simple functional keras model with one layer."""
import tensorflow.compat.v2 as tf
import numpy as np
import keras
from keras.distribute import model_collection_base
from keras.optimizer_v2 import gradient_descent
_BATCH_SIZE = 10
def _get_data_for_simple_models():
x_train = tf.constant(np.random.rand(1000, 3), dtype=tf.float32)
y_train = tf.constant(np.random.rand(1000, 5), dtype=tf.float32)
x_predict = tf.constant(
np.random.rand(1000, 3), dtype=tf.float32)
return x_train, y_train, x_predict
class SimpleFunctionalModel(model_collection_base.ModelAndInput):
"""A simple functional model and its inputs."""
def get_model(self, **kwargs):
output_name = 'output_1'
x = keras.layers.Input(shape=(3,), dtype=tf.float32)
y = keras.layers.Dense(5, dtype=tf.float32, name=output_name)(x)
model = keras.Model(inputs=x, outputs=y)
optimizer = gradient_descent.SGD(learning_rate=0.001)
model.compile(
loss='mse',
metrics=['mae'],
optimizer=optimizer)
return model
def get_data(self):
return _get_data_for_simple_models()
def get_batch_size(self):
return _BATCH_SIZE
class SimpleSequentialModel(model_collection_base.ModelAndInput):
"""A simple sequential model and its inputs."""
def get_model(self, **kwargs):
output_name = 'output_1'
model = keras.Sequential()
y = keras.layers.Dense(
5, dtype=tf.float32, name=output_name, input_dim=3)
model.add(y)
optimizer = gradient_descent.SGD(learning_rate=0.001)
model.compile(
loss='mse',
metrics=['mae'],
optimizer=optimizer)
return model
def get_data(self):
return _get_data_for_simple_models()
def get_batch_size(self):
return _BATCH_SIZE
class _SimpleModel(keras.Model):
def __init__(self):
super(_SimpleModel, self).__init__()
self._dense_layer = keras.layers.Dense(5, dtype=tf.float32)
def call(self, inputs):
return self._dense_layer(inputs)
class SimpleSubclassModel(model_collection_base.ModelAndInput):
"""A simple subclass model and its data."""
def get_model(self, **kwargs):
model = _SimpleModel()
optimizer = gradient_descent.SGD(learning_rate=0.001)
model.compile(
loss='mse',
metrics=['mae'],
cloning=False,
optimizer=optimizer)
return model
def get_data(self):
return _get_data_for_simple_models()
def get_batch_size(self):
return _BATCH_SIZE
class _SimpleModule(tf.Module):
def __init__(self):
self.v = tf.Variable(3.0)
@tf.function
def __call__(self, x):
return self.v * x
class SimpleTFModuleModel(model_collection_base.ModelAndInput):
"""A simple model based on tf.Module and its data."""
def get_model(self, **kwargs):
model = _SimpleModule()
return model
def get_data(self):
return _get_data_for_simple_models()
def get_batch_size(self):
return _BATCH_SIZE
Classes
class SimpleFunctionalModel
-
A simple functional model and its inputs.
Expand source code
class SimpleFunctionalModel(model_collection_base.ModelAndInput): """A simple functional model and its inputs.""" def get_model(self, **kwargs): output_name = 'output_1' x = keras.layers.Input(shape=(3,), dtype=tf.float32) y = keras.layers.Dense(5, dtype=tf.float32, name=output_name)(x) model = keras.Model(inputs=x, outputs=y) optimizer = gradient_descent.SGD(learning_rate=0.001) model.compile( loss='mse', metrics=['mae'], optimizer=optimizer) return model def get_data(self): return _get_data_for_simple_models() def get_batch_size(self): return _BATCH_SIZE
Ancestors
Inherited members
class SimpleSequentialModel
-
A simple sequential model and its inputs.
Expand source code
class SimpleSequentialModel(model_collection_base.ModelAndInput): """A simple sequential model and its inputs.""" def get_model(self, **kwargs): output_name = 'output_1' model = keras.Sequential() y = keras.layers.Dense( 5, dtype=tf.float32, name=output_name, input_dim=3) model.add(y) optimizer = gradient_descent.SGD(learning_rate=0.001) model.compile( loss='mse', metrics=['mae'], optimizer=optimizer) return model def get_data(self): return _get_data_for_simple_models() def get_batch_size(self): return _BATCH_SIZE
Ancestors
Inherited members
class SimpleSubclassModel
-
A simple subclass model and its data.
Expand source code
class SimpleSubclassModel(model_collection_base.ModelAndInput): """A simple subclass model and its data.""" def get_model(self, **kwargs): model = _SimpleModel() optimizer = gradient_descent.SGD(learning_rate=0.001) model.compile( loss='mse', metrics=['mae'], cloning=False, optimizer=optimizer) return model def get_data(self): return _get_data_for_simple_models() def get_batch_size(self): return _BATCH_SIZE
Ancestors
Inherited members
class SimpleTFModuleModel
-
A simple model based on tf.Module and its data.
Expand source code
class SimpleTFModuleModel(model_collection_base.ModelAndInput): """A simple model based on tf.Module and its data.""" def get_model(self, **kwargs): model = _SimpleModule() return model def get_data(self): return _get_data_for_simple_models() def get_batch_size(self): return _BATCH_SIZE
Ancestors
Inherited members