def test_trace_multi_io_model_outputs(self):
input_dim = 5
num_classes = 3
num_classes_b = 4
input_a = keras.layers.Input(shape=(input_dim, ), name='input_a')
input_b = keras.layers.Input(shape=(input_dim, ), name='input_b')
dense = keras.layers.Dense(num_classes, name='dense')
dense2 = keras.layers.Dense(num_classes_b, name='dense2')
dropout = keras.layers.Dropout(0.5, name='dropout')
branch_a = [input_a, dense]
branch_b = [input_b, dense, dense2, dropout]
model = testing_utils.get_multi_io_model(branch_a, branch_b)
input_a_ts = tf.constant(
np.random.random((10, input_dim)).astype(np.float32))
input_b_ts = tf.constant(
np.random.random((10, input_dim)).astype(np.float32))
if testing_utils.get_model_type() == 'subclass':
with self.assertRaisesRegex(ValueError,
'input shapes have not been set'):
saving_utils.trace_model_call(model)
model.compile(optimizer='sgd',
loss='mse',
run_eagerly=testing_utils.should_run_eagerly())
model.fit(x=[
np.random.random((8, input_dim)).astype(np.float32),
np.random.random((8, input_dim)).astype(np.float32)
],
y=[
np.random.random((8, num_classes)).astype(np.float32),
np.random.random((8, num_classes_b)).astype(np.float32)
],
epochs=2)
fn = saving_utils.trace_model_call(model)
# tf.function requires that the input structures match when calling a
# ConcreteFunction. For some reason V1 models defines the inputs as a list,
# while V2 models sets the inputs as a tuple.
if (not tf.executing_eagerly()
and testing_utils.get_model_type() != 'functional'):
signature_outputs = fn([input_a_ts, input_b_ts])
else:
signature_outputs = fn((input_a_ts, input_b_ts))
outputs = model([input_a_ts, input_b_ts])
if model.output_names:
expected_outputs = {
model.output_names[0]: outputs[0],
model.output_names[1]: outputs[1]
}
else:
expected_outputs = {'output_1': outputs[0], 'output_2': outputs[1]}
self._assert_all_close(expected_outputs, signature_outputs)
def get_multi_io_temporal_model():
timesteps = 2
inp_1 = layers.Input(shape=(1, ), name='input_1')
inp_2 = layers.Input(shape=(1, ), name='input_2')
x = layers.RepeatVector(timesteps)
out_1 = layers.TimeDistributed(Bias(), name='output_1')
out_2 = layers.TimeDistributed(Bias(), name='output_2')
branch_a = [inp_1, x, out_1]
branch_b = [inp_2, x, out_2]
return testing_utils.get_multi_io_model(branch_a, branch_b)
def get_multi_io_model():
inp_1 = layers.Input(shape=(1,), name='input_1')
inp_2 = layers.Input(shape=(1,), name='input_2')
x = layers.Dense(3, kernel_initializer='ones', trainable=False)
out_1 = layers.Dense(
1, kernel_initializer='ones', name='output_1', trainable=False)
out_2 = layers.Dense(
1, kernel_initializer='ones', name='output_2', trainable=False)
branch_a = [inp_1, x, out_1]
branch_b = [inp_2, x, out_2]
return testing_utils.get_multi_io_model(branch_a, branch_b)
def test_trace_multi_io_model_outputs(self):
input_dim = 5
num_classes = 3
num_classes_b = 4
input_a = keras.layers.Input(shape=(input_dim, ), name='input_a')
input_b = keras.layers.Input(shape=(input_dim, ), name='input_b')
dense = keras.layers.Dense(num_classes, name='dense')
dense2 = keras.layers.Dense(num_classes_b, name='dense2')
dropout = keras.layers.Dropout(0.5, name='dropout')
branch_a = [input_a, dense]
branch_b = [input_b, dense, dense2, dropout]
model = testing_utils.get_multi_io_model(branch_a, branch_b)
input_a_np = np.random.random((10, input_dim)).astype(np.float32)
input_b_np = np.random.random((10, input_dim)).astype(np.float32)
if testing_utils.get_model_type() == 'subclass':
with self.assertRaisesRegex(ValueError,
'input shapes have not been set'):
saving_utils.trace_model_call(model)
model.compile(optimizer='sgd',
loss='mse',
run_eagerly=testing_utils.should_run_eagerly())
model.fit(x=[
np.random.random((8, input_dim)).astype(np.float32),
np.random.random((8, input_dim)).astype(np.float32)
],
y=[
np.random.random((8, num_classes)).astype(np.float32),
np.random.random((8, num_classes_b)).astype(np.float32)
],
epochs=2)
fn = saving_utils.trace_model_call(model)
signature_outputs = fn([input_a_np, input_b_np])
outputs = model([input_a_np, input_b_np])
if model.output_names:
expected_outputs = {
model.output_names[0]: outputs[0],
model.output_names[1]: outputs[1]
}
else:
expected_outputs = {'output_1': outputs[0], 'output_2': outputs[1]}
self._assert_all_close(expected_outputs, signature_outputs)
def test_training_and_eval_methods_on_multi_input_output_dataset(self):
input_a = keras.layers.Input(shape=(3, ), name='input_1')
input_b = keras.layers.Input(shape=(3, ), name='input_2')
dense = keras.layers.Dense(4, name='dense')
dropout = keras.layers.Dropout(0.5, name='dropout')
branch_a = [input_a, dense]
branch_b = [input_b, dense, dropout]
model = testing_utils.get_multi_io_model(branch_a, branch_b)
model.compile(optimizer='rmsprop',
loss='mse',
run_eagerly=testing_utils.should_run_eagerly())
input_a_np = np.random.random((10, 3)).astype(dtype=np.float32)
input_b_np = np.random.random((10, 3)).astype(dtype=np.float32)
output_d_np = np.random.random((10, 4)).astype(dtype=np.float32)
output_e_np = np.random.random((10, 4)).astype(dtype=np.float32)
# Test with tuples
dataset_tuple = tf.data.Dataset.from_tensor_slices(
((input_a_np, input_b_np), (output_d_np, output_e_np)))
dataset_tuple = dataset_tuple.repeat(100)
dataset_tuple = dataset_tuple.batch(10)
model.fit(dataset_tuple, epochs=1, steps_per_epoch=2, verbose=1)
model.evaluate(dataset_tuple, steps=2, verbose=1)
# Test with dict
input_dict = {'input_1': input_a_np, 'input_2': input_b_np}
if testing_utils.get_model_type() == 'subclass':
output_dict = {'output_1': output_d_np, 'output_2': output_e_np}
else:
output_dict = {'dense': output_d_np, 'dropout': output_e_np}
dataset_dict = tf.data.Dataset.from_tensor_slices(
(input_dict, output_dict))
dataset_dict = dataset_dict.repeat(100)
dataset_dict = dataset_dict.batch(10)
model.fit(dataset_dict, epochs=1, steps_per_epoch=2, verbose=1)
model.evaluate(dataset_dict, steps=2, verbose=1)
predict_dataset_dict = tf.data.Dataset.from_tensor_slices(input_dict)
predict_dataset_dict = predict_dataset_dict.repeat(100)
predict_dataset_dict = predict_dataset_dict.batch(10)
model.predict(predict_dataset_dict, steps=1)
def test_model_methods_with_eager_tensors_multi_io(self):
if not tf.executing_eagerly():
# Only test V2 Function and V2 Eager modes, as V1 Graph mode with
# symbolic tensors has different requirements.
return
input_a = keras.layers.Input(shape=(3, ), name='input_a')
input_b = keras.layers.Input(shape=(3, ), name='input_b')
dense = keras.layers.Dense(4, name='dense')
dropout = keras.layers.Dropout(0.5, name='dropout')
model = testing_utils.get_multi_io_model([input_a, dense],
[input_b, dense, dropout])
optimizer = rmsprop.RMSprop(learning_rate=0.001)
loss = 'mse'
loss_weights = [1., 0.5]
metrics = ['mae', metrics_module.CategoricalAccuracy()]
model.compile(optimizer,
loss,
metrics=metrics,
loss_weights=loss_weights,
run_eagerly=testing_utils.should_run_eagerly(),
sample_weight_mode=None)
input_a = tf.zeros(shape=(10, 3))
input_b = tf.zeros(shape=(10, 3))
target_a = tf.zeros(shape=(10, 4))
target_b = tf.zeros(shape=(10, 4))
model.fit([input_a, input_b], [target_a, target_b],
epochs=1,
batch_size=5,
verbose=0)
# Test: no shuffle.
model.fit([input_a, input_b], [target_a, target_b],
epochs=1,
batch_size=5,
verbose=0,
shuffle=False)
# Test: validation data.
model.fit([input_a, input_b], [target_a, target_b],
epochs=1,
batch_size=2,
verbose=0,
validation_data=([input_a, input_b], [target_a, target_b]))
model.train_on_batch([input_a, input_b], [target_a, target_b])
model.predict([input_a, input_b], batch_size=5)
model.evaluate([input_a, input_b], [target_a, target_b],
batch_size=2,
verbose=0)
model.test_on_batch([input_a, input_b], [target_a, target_b])
# Test: mix np and tensors.
input_b = np.zeros(shape=(10, 3)).astype('float32')
target_b = np.zeros(shape=(10, 4)).astype('float32')
model.fit([input_a, input_b], [target_a, target_b],
epochs=1,
batch_size=5,
verbose=0)
model.fit([input_a, input_b], [target_a, target_b],
epochs=1,
batch_size=2,
verbose=0,
validation_data=([input_a, input_b], [target_a, target_b]))
model.fit([input_a, input_b], [target_a, target_b],
epochs=1,
batch_size=5,
verbose=0,
shuffle=False)
model.train_on_batch([input_a, input_b], [target_a, target_b])
model.predict([input_a, input_b], batch_size=5)
model.evaluate([input_a, input_b], [target_a, target_b],
batch_size=2,
verbose=0)
model.test_on_batch([input_a, input_b], [target_a, target_b])