def input_layer(features, feature_columns):
if tf.__version__ < "1.15.0":
from tensorflow.python.feature_column import feature_column_v2 as fc
return fc.FeatureLayer(feature_columns)(features)
elif tf.__version__ < "2.0.0":
return tf.feature_column.input_layer(features, feature_columns)
else:
return tf.compat.v1.feature_column.input_layer(features,
feature_columns)
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)
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)
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))
def __init__(self,
units,
hidden_units,
feature_columns,
activation_fn,
dropout,
input_layer_partitioner,
batch_norm,
name=None,
**kwargs):
super(_DNNModel, self).__init__(name=name, **kwargs)
if feature_column_v2.is_feature_column_v2(feature_columns):
self._input_layer = feature_column_v2.FeatureLayer(
feature_columns=feature_columns, name='input_layer')
else:
self._input_layer = feature_column.InputLayer(
feature_columns=feature_columns,
name='input_layer',
create_scope_now=False)
self._add_layer(self._input_layer, 'input_layer')
self._dropout = dropout
self._batch_norm = batch_norm
self._hidden_layers = []
self._dropout_layers = []
self._batch_norm_layers = []
self._hidden_layer_scope_names = []
for layer_id, num_hidden_units in enumerate(hidden_units):
with variable_scope.variable_scope('hiddenlayer_%d' %
layer_id) as hidden_layer_scope:
hidden_layer = core_layers.Dense(
units=num_hidden_units,
activation=activation_fn,
kernel_initializer=init_ops.glorot_uniform_initializer(),
name=hidden_layer_scope,
_scope=hidden_layer_scope)
self._add_layer(hidden_layer, hidden_layer_scope.name)
self._hidden_layer_scope_names.append(hidden_layer_scope.name)
self._hidden_layers.append(hidden_layer)
if self._dropout is not None:
dropout_layer = core_layers.Dropout(rate=self._dropout)
self._add_layer(dropout_layer, dropout_layer.name)
self._dropout_layers.append(dropout_layer)
if self._batch_norm:
batch_norm_layer = normalization.BatchNormalization(
# The default momentum 0.99 actually crashes on certain
# problem, so here we use 0.999, which is the default of
# tf.contrib.layers.batch_norm.
momentum=0.999,
trainable=True,
name='batchnorm_%d' % layer_id,
_scope='batchnorm_%d' % layer_id)
self._add_layer(batch_norm_layer, batch_norm_layer.name)
self._batch_norm_layers.append(batch_norm_layer)
with variable_scope.variable_scope('logits') as logits_scope:
self._logits_layer = core_layers.Dense(
units=units,
activation=None,
kernel_initializer=init_ops.glorot_uniform_initializer(),
name=logits_scope,
_scope=logits_scope)
self._add_layer(self._logits_layer, logits_scope.name)
self._logits_scope_name = logits_scope.name
self._input_layer_partitioner = input_layer_partitioner
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 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))