278 lines
12 KiB
Python
278 lines
12 KiB
Python
# Copyright 2019 The TensorFlow Authors. All Rights Reserved.
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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# ==============================================================================
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"""Tests for Keras Premade WideNDeep models."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import numpy as np
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from tensorflow.python.feature_column import feature_column_v2 as fc
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from tensorflow.python.keras import keras_parameterized
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from tensorflow.python.keras import testing_utils
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from tensorflow.python.keras.engine import input_layer
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from tensorflow.python.keras.engine import sequential
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from tensorflow.python.keras.engine import training
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from tensorflow.python.keras.feature_column import dense_features_v2
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from tensorflow.python.keras.layers import core
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from tensorflow.python.keras.optimizer_v2 import gradient_descent
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from tensorflow.python.keras.premade import linear
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from tensorflow.python.keras.premade import wide_deep
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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@keras_parameterized.run_all_keras_modes(always_skip_v1=True)
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class WideDeepModelTest(keras_parameterized.TestCase):
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def test_wide_deep_model(self):
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linear_model = linear.LinearModel(units=1)
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dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
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wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
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linear_inp = np.random.uniform(low=-5, high=5, size=(64, 2))
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dnn_inp = np.random.uniform(low=-5, high=5, size=(64, 3))
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inputs = [linear_inp, dnn_inp]
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output = .3 * linear_inp[:, 0] + .2 * dnn_inp[:, 1]
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wide_deep_model.compile(
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optimizer=['sgd', 'adam'],
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loss='mse',
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metrics=[],
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run_eagerly=testing_utils.should_run_eagerly())
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wide_deep_model.fit(inputs, output, epochs=5)
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self.assertTrue(wide_deep_model.built)
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def test_wide_deep_model_backprop(self):
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with self.cached_session():
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linear_model = linear.LinearModel(units=1, kernel_initializer='zeros')
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dnn_model = sequential.Sequential(
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[core.Dense(units=1, kernel_initializer='zeros')])
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wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
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linear_inp = np.array([1.])
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dnn_inp = np.array([1.])
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inputs = [linear_inp, dnn_inp]
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output = linear_inp + 2 * dnn_inp
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linear_opt = gradient_descent.SGD(learning_rate=.1)
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dnn_opt = gradient_descent.SGD(learning_rate=.3)
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wide_deep_model.compile(
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optimizer=[linear_opt, dnn_opt],
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loss='mse',
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metrics=[],
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run_eagerly=testing_utils.should_run_eagerly())
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self.evaluate(variables.global_variables_initializer())
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wide_deep_model.fit(inputs, output, epochs=1)
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self.assertAllClose(
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[[0.6]],
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self.evaluate(wide_deep_model.linear_model.dense_layers[0].kernel))
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self.assertAllClose([[1.8]],
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self.evaluate(
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wide_deep_model.dnn_model.layers[0].kernel))
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def test_wide_deep_model_with_single_input(self):
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linear_model = linear.LinearModel(units=1)
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dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
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wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
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inputs = np.random.uniform(low=-5, high=5, size=(64, 3))
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output = .3 * inputs[:, 0]
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wide_deep_model.compile(
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optimizer=['sgd', 'adam'],
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loss='mse',
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metrics=[],
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run_eagerly=testing_utils.should_run_eagerly())
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wide_deep_model.fit(inputs, output, epochs=5)
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def test_wide_deep_model_with_multi_outputs(self):
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inp = input_layer.Input(shape=(1,), name='linear')
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l = linear.LinearModel(units=2, use_bias=False)(inp)
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l1, l2 = array_ops.split(l, num_or_size_splits=2, axis=1)
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linear_model = training.Model(inp, [l1, l2])
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linear_model.set_weights([np.asarray([[0.5, 0.3]])])
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h = core.Dense(units=2, use_bias=False)(inp)
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h1, h2 = array_ops.split(h, num_or_size_splits=2, axis=1)
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dnn_model = training.Model(inp, [h1, h2])
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dnn_model.set_weights([np.asarray([[0.1, -0.5]])])
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wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
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inp_np = np.asarray([[1.]])
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out1, out2 = wide_deep_model(inp_np)
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# output should be (0.5 + 0.1), and (0.3 - 0.5)
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self.assertAllClose([[0.6]], out1)
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self.assertAllClose([[-0.2]], out2)
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wide_deep_model = wide_deep.WideDeepModel(
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linear_model, dnn_model, activation='relu')
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out1, out2 = wide_deep_model(inp_np)
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# output should be relu((0.5 + 0.1)), and relu((0.3 - 0.5))
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self.assertAllClose([[0.6]], out1)
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self.assertAllClose([[0.]], out2)
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def test_wide_deep_model_with_single_optimizer(self):
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linear_model = linear.LinearModel(units=1)
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dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
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wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
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linear_inp = np.random.uniform(low=-5, high=5, size=(64, 2))
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dnn_inp = np.random.uniform(low=-5, high=5, size=(64, 3))
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inputs = [linear_inp, dnn_inp]
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output = .3 * linear_inp[:, 0] + .2 * dnn_inp[:, 1]
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wide_deep_model.compile(
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optimizer='sgd',
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loss='mse',
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metrics=[],
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run_eagerly=testing_utils.should_run_eagerly())
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wide_deep_model.fit(inputs, output, epochs=5)
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self.assertTrue(wide_deep_model.built)
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def test_wide_deep_model_as_layer(self):
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linear_model = linear.LinearModel(units=1)
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dnn_model = sequential.Sequential([core.Dense(units=1)])
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linear_input = input_layer.Input(shape=(3,), name='linear')
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dnn_input = input_layer.Input(shape=(5,), name='dnn')
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wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
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wide_deep_output = wide_deep_model((linear_input, dnn_input))
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input_b = input_layer.Input(shape=(1,), name='b')
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output_b = core.Dense(units=1)(input_b)
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model = training.Model(
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inputs=[linear_input, dnn_input, input_b],
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outputs=[wide_deep_output + output_b])
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linear_input_np = np.random.uniform(low=-5, high=5, size=(64, 3))
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dnn_input_np = np.random.uniform(low=-5, high=5, size=(64, 5))
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input_b_np = np.random.uniform(low=-5, high=5, size=(64,))
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output_np = linear_input_np[:, 0] + .2 * dnn_input_np[:, 1] + input_b_np
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model.compile(
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optimizer='sgd',
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loss='mse',
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metrics=[],
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run_eagerly=testing_utils.should_run_eagerly())
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model.fit([linear_input_np, dnn_input_np, input_b_np], output_np, epochs=5)
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def test_wide_deep_model_with_sub_model_trained(self):
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linear_model = linear.LinearModel(units=1)
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dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
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wide_deep_model = wide_deep.WideDeepModel(
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linear.LinearModel(units=1),
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sequential.Sequential([core.Dense(units=1, input_dim=3)]))
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linear_inp = np.random.uniform(low=-5, high=5, size=(64, 2))
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dnn_inp = np.random.uniform(low=-5, high=5, size=(64, 3))
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inputs = [linear_inp, dnn_inp]
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output = .3 * linear_inp[:, 0] + .2 * dnn_inp[:, 1]
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linear_model.compile(
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optimizer='sgd',
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loss='mse',
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metrics=[],
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run_eagerly=testing_utils.should_run_eagerly())
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dnn_model.compile(
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optimizer='adam',
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loss='mse',
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metrics=[],
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run_eagerly=testing_utils.should_run_eagerly())
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linear_model.fit(linear_inp, output, epochs=50)
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dnn_model.fit(dnn_inp, output, epochs=50)
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wide_deep_model.compile(
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optimizer=['sgd', 'adam'],
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loss='mse',
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metrics=[],
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run_eagerly=testing_utils.should_run_eagerly())
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wide_deep_model.fit(inputs, output, epochs=50)
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# This test is an example for cases where linear and dnn model accepts
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# same raw input and same transformed inputs, i.e., the raw input is
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# categorical, and both linear and dnn model accept one hot encoding.
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def test_wide_deep_model_with_single_feature_column(self):
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vocab_list = ['alpha', 'beta', 'gamma']
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vocab_val = [0.4, 0.6, 0.9]
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data = np.random.choice(vocab_list, size=256)
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y = np.zeros_like(data, dtype=np.float32)
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for vocab, val in zip(vocab_list, vocab_val):
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indices = np.where(data == vocab)
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y[indices] = val + np.random.uniform(
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low=-0.01, high=0.01, size=indices[0].shape)
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cat_column = fc.categorical_column_with_vocabulary_list(
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key='symbol', vocabulary_list=vocab_list)
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ind_column = fc.indicator_column(cat_column)
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dense_feature_layer = dense_features_v2.DenseFeatures([ind_column])
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linear_model = linear.LinearModel(
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use_bias=False, kernel_initializer='zeros')
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dnn_model = sequential.Sequential([core.Dense(units=1)])
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wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
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combined = sequential.Sequential([dense_feature_layer, wide_deep_model])
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opt = gradient_descent.SGD(learning_rate=0.1)
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combined.compile(
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opt,
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'mse', [],
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run_eagerly=testing_utils.should_run_eagerly())
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combined.fit(x={'symbol': data}, y=y, batch_size=32, epochs=10)
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# This test is an example for cases where linear and dnn model accepts
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# same raw input but different transformed inputs, i.e,. the raw input is
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# categorical, and linear model accepts one hot encoding, while dnn model
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# accepts embedding encoding.
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def test_wide_deep_model_with_two_feature_columns(self):
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vocab_list = ['alpha', 'beta', 'gamma']
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vocab_val = [0.4, 0.6, 0.9]
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data = np.random.choice(vocab_list, size=256)
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y = np.zeros_like(data, dtype=np.float32)
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for vocab, val in zip(vocab_list, vocab_val):
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indices = np.where(data == vocab)
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y[indices] = val + np.random.uniform(
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low=-0.01, high=0.01, size=indices[0].shape)
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cat_column = fc.categorical_column_with_vocabulary_list(
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key='symbol', vocabulary_list=vocab_list)
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ind_column = fc.indicator_column(cat_column)
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emb_column = fc.embedding_column(cat_column, dimension=5)
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linear_feature_layer = dense_features_v2.DenseFeatures([ind_column])
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linear_model = linear.LinearModel(
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use_bias=False, kernel_initializer='zeros')
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combined_linear = sequential.Sequential(
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[linear_feature_layer, linear_model])
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dnn_model = sequential.Sequential([core.Dense(units=1)])
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dnn_feature_layer = dense_features_v2.DenseFeatures([emb_column])
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combined_dnn = sequential.Sequential([dnn_feature_layer, dnn_model])
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wide_deep_model = wide_deep.WideDeepModel(combined_linear, combined_dnn)
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opt = gradient_descent.SGD(learning_rate=0.1)
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wide_deep_model.compile(
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opt,
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'mse', [],
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run_eagerly=testing_utils.should_run_eagerly())
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wide_deep_model.fit(x={'symbol': data}, y=y, batch_size=32, epochs=10)
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def test_config(self):
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linear_model = linear.LinearModel(units=1)
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dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
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wide_deep_model = wide_deep.WideDeepModel(linear_model, dnn_model)
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config = wide_deep_model.get_config()
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cloned_wide_deep_model = wide_deep.WideDeepModel.from_config(config)
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self.assertEqual(linear_model.units,
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cloned_wide_deep_model.linear_model.units)
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self.assertEqual(dnn_model.layers[0].units,
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cloned_wide_deep_model.dnn_model.layers[0].units)
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def test_config_with_custom_objects(self):
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def my_activation(x):
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return x
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linear_model = linear.LinearModel(units=1)
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dnn_model = sequential.Sequential([core.Dense(units=1, input_dim=3)])
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wide_deep_model = wide_deep.WideDeepModel(
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linear_model, dnn_model, activation=my_activation)
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config = wide_deep_model.get_config()
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cloned_wide_deep_model = wide_deep.WideDeepModel.from_config(
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config, custom_objects={'my_activation': my_activation})
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self.assertEqual(cloned_wide_deep_model.activation, my_activation)
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if __name__ == '__main__':
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test.main()
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