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+# 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()
generated by cgit v1.2.3 (git 2.39.1) at 2024-06-09 04:16:15 +0000