1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
|
# Copyright 2018 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.
# ==============================================================================
"""Tests specific to Feature Columns integration."""
from __future__ import absolute_import
from __future__ import division
from __future__ import print_function
import numpy as np
from tensorflow.python import keras
from tensorflow.python.data.ops import dataset_ops
from tensorflow.python.feature_column import feature_column_v2 as fc
from tensorflow.python.framework import test_util as tf_test_util
from tensorflow.python.keras import metrics as metrics_module
from tensorflow.python.platform import test
from tensorflow.python.training import rmsprop
class TestDNNModel(keras.models.Model):
def __init__(self, feature_columns, units, name=None, **kwargs):
super(TestDNNModel, self).__init__(name=name, **kwargs)
self._input_layer = fc.FeatureLayer(feature_columns, name='input_layer')
self._dense_layer = keras.layers.Dense(units, name='dense_layer')
def call(self, features):
net = self._input_layer(features)
net = self._dense_layer(net)
return net
class FeatureColumnsIntegrationTest(test.TestCase):
"""Most Sequential model API tests are covered in `training_test.py`.
"""
@tf_test_util.run_in_graph_and_eager_modes
def test_sequential_model(self):
columns = [fc.numeric_column('a')]
model = keras.models.Sequential([
fc.FeatureLayer(columns),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dense(20, activation='softmax')
])
model.compile(
optimizer=rmsprop.RMSPropOptimizer(1e-3),
loss='categorical_crossentropy',
metrics=['accuracy'])
x = {'a': np.random.random((10, 1))}
y = np.random.randint(20, size=(10, 1))
y = keras.utils.to_categorical(y, num_classes=20)
model.fit(x, y, epochs=1, batch_size=5)
model.fit(x, y, epochs=1, batch_size=5)
model.evaluate(x, y, batch_size=5)
model.predict(x, batch_size=5)
@tf_test_util.run_in_graph_and_eager_modes
def test_sequential_model_with_ds_input(self):
columns = [fc.numeric_column('a')]
model = keras.models.Sequential([
fc.FeatureLayer(columns),
keras.layers.Dense(64, activation='relu'),
keras.layers.Dense(20, activation='softmax')
])
model.compile(
optimizer=rmsprop.RMSPropOptimizer(1e-3),
loss='categorical_crossentropy',
metrics=['accuracy'])
y = np.random.randint(20, size=(100, 1))
y = keras.utils.to_categorical(y, num_classes=20)
x = {'a': np.random.random((100, 1))}
ds1 = dataset_ops.Dataset.from_tensor_slices(x)
ds2 = dataset_ops.Dataset.from_tensor_slices(y)
ds = dataset_ops.Dataset.zip((ds1, ds2)).batch(5)
model.fit(ds, steps_per_epoch=1)
model.fit(ds, steps_per_epoch=1)
model.evaluate(ds, steps=1)
model.predict(ds, steps=1)
@tf_test_util.run_in_graph_and_eager_modes
def test_subclassed_model_with_feature_columns(self):
col_a = fc.numeric_column('a')
col_b = fc.numeric_column('b')
dnn_model = TestDNNModel([col_a, col_b], 20)
dnn_model.compile(
optimizer=rmsprop.RMSPropOptimizer(learning_rate=0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
x = {'a': np.random.random((10, 1)), 'b': np.random.random((10, 1))}
y = np.random.randint(20, size=(10, 1))
y = keras.utils.to_categorical(y, num_classes=20)
dnn_model.fit(x=x, y=y, epochs=1, batch_size=5)
dnn_model.fit(x=x, y=y, epochs=1, batch_size=5)
dnn_model.evaluate(x=x, y=y, batch_size=5)
dnn_model.predict(x=x, batch_size=5)
@tf_test_util.run_in_graph_and_eager_modes
def test_subclassed_model_with_feature_columns_with_ds_input(self):
col_a = fc.numeric_column('a')
col_b = fc.numeric_column('b')
dnn_model = TestDNNModel([col_a, col_b], 20)
dnn_model.compile(
optimizer=rmsprop.RMSPropOptimizer(learning_rate=0.001),
loss='categorical_crossentropy',
metrics=['accuracy'])
y = np.random.randint(20, size=(100, 1))
y = keras.utils.to_categorical(y, num_classes=20)
x = {'a': np.random.random((100, 1)), 'b': np.random.random((100, 1))}
ds1 = dataset_ops.Dataset.from_tensor_slices(x)
ds2 = dataset_ops.Dataset.from_tensor_slices(y)
ds = dataset_ops.Dataset.zip((ds1, ds2)).batch(5)
dnn_model.fit(ds, steps_per_epoch=1)
dnn_model.fit(ds, steps_per_epoch=1)
dnn_model.evaluate(ds, steps=1)
dnn_model.predict(ds, steps=1)
@tf_test_util.run_in_graph_and_eager_modes
def DISABLED_test_function_model_feature_layer_input(self):
col_a = fc.numeric_column('a')
col_b = fc.numeric_column('b')
feature_layer = fc.FeatureLayer([col_a, col_b], name='fc')
dense = keras.layers.Dense(4)
# This seems problematic.... We probably need something for FeatureLayer
# the way Input is for InputLayer.
output = dense(feature_layer)
model = keras.models.Model([feature_layer], [output])
optimizer = rmsprop.RMSPropOptimizer(learning_rate=0.001)
loss = 'mse'
loss_weights = [1., 0.5]
model.compile(
optimizer,
loss,
metrics=[metrics_module.CategoricalAccuracy(), 'mae'],
loss_weights=loss_weights)
data = ({'a': np.arange(10), 'b': np.arange(10)}, np.arange(10, 20))
print(model.fit(*data, epochs=1))
@tf_test_util.run_in_graph_and_eager_modes
def DISABLED_test_function_model_multiple_feature_layer_inputs(self):
col_a = fc.numeric_column('a')
col_b = fc.numeric_column('b')
col_c = fc.numeric_column('c')
fc1 = fc.FeatureLayer([col_a, col_b], name='fc1')
fc2 = fc.FeatureLayer([col_b, col_c], name='fc2')
dense = keras.layers.Dense(4)
# This seems problematic.... We probably need something for FeatureLayer
# the way Input is for InputLayer.
output = dense(fc1) + dense(fc2)
model = keras.models.Model([fc1, fc2], [output])
optimizer = rmsprop.RMSPropOptimizer(learning_rate=0.001)
loss = 'mse'
loss_weights = [1., 0.5]
model.compile(
optimizer,
loss,
metrics=[metrics_module.CategoricalAccuracy(), 'mae'],
loss_weights=loss_weights)
data_list = ([{
'a': np.arange(10),
'b': np.arange(10)
}, {
'b': np.arange(10),
'c': np.arange(10)
}], np.arange(10, 100))
print(model.fit(*data_list, epochs=1))
data_bloated_list = ([{
'a': np.arange(10),
'b': np.arange(10),
'c': np.arange(10)
}, {
'a': np.arange(10),
'b': np.arange(10),
'c': np.arange(10)
}], np.arange(10, 100))
print(model.fit(*data_bloated_list, epochs=1))
data_dict = ({
'fc1': {
'a': np.arange(10),
'b': np.arange(10)
},
'fc2': {
'b': np.arange(10),
'c': np.arange(10)
}
}, np.arange(10, 100))
print(model.fit(*data_dict, epochs=1))
data_bloated_dict = ({
'fc1': {
'a': np.arange(10),
'b': np.arange(10),
'c': np.arange(10)
},
'fc2': {
'a': np.arange(10),
'b': np.arange(10),
'c': np.arange(10)
}
}, np.arange(10, 100))
print(model.fit(*data_bloated_dict, epochs=1))
if __name__ == '__main__':
test.main()
|
