def test_warnings():
a = Input(shape=(3,), name='input_a')
b = Input(shape=(3,), name='input_b')
a_2 = Dense(4, name='dense_1')(a)
dp = Dropout(0.5, name='dropout')
b_2 = dp(b)
model = Model([a, b], [a_2, b_2])
optimizer = 'rmsprop'
loss = 'mse'
loss_weights = [1., 0.5]
model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
sample_weight_mode=None)
def gen_data(batch_sz):
while True:
yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
[np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])
with pytest.warns(Warning) as w:
out = model.fit_generator(gen_data(4), steps_per_epoch=10, use_multiprocessing=True, workers=2)
warning_raised = any(['Sequence' in str(w_.message) for w_ in w])
assert warning_raised, 'No warning raised when using generator with processes.'
with pytest.warns(None) as w:
out = model.fit_generator(RandomSequence(3), steps_per_epoch=4, use_multiprocessing=True, workers=2)
assert all(['Sequence' not in str(w_.message) for w_ in w]), 'A warning was raised for Sequence.'
def run_parallel_test(data_generator):
a = Input(shape=(3, ), name='input_a')
b = Input(shape=(3, ), name='input_b')
a_2 = Dense(4, name='dense_1')(a)
dp = Dropout(0.5, name='dropout')
b_2 = dp(b)
optimizer = 'rmsprop'
loss = 'mse'
loss_weights = [1., 0.5]
model = Model([a, b], [a_2, b_2])
model = make_parallel(model, 2)
model.compile(optimizer,
loss,
metrics=[],
loss_weights=loss_weights,
sample_weight_mode=None)
trained_epochs = []
tracker_cb = LambdaCallback(
on_epoch_begin=lambda epoch, logs: trained_epochs.append(epoch))
model.fit_generator(data_generator(4),
steps_per_epoch=3,
epochs=5,
initial_epoch=2,
callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
def test_warnings():
a = Input(shape=(3,), name='input_a')
b = Input(shape=(3,), name='input_b')
a_2 = Dense(4, name='dense_1')(a)
dp = Dropout(0.5, name='dropout')
b_2 = dp(b)
model = Model([a, b], [a_2, b_2])
optimizer = 'rmsprop'
loss = 'mse'
loss_weights = [1., 0.5]
model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
sample_weight_mode=None)
def gen_data(batch_sz):
while True:
yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
[np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])
with pytest.warns(Warning) as w:
out = model.fit_generator(gen_data(4), steps_per_epoch=10, use_multiprocessing=True, workers=2)
warning_raised = any(['Sequence' in str(w_.message) for w_ in w])
assert warning_raised, 'No warning raised when using generator with processes.'
with pytest.warns(None) as w:
out = model.fit_generator(RandomSequence(3), steps_per_epoch=4, use_multiprocessing=True, workers=2)
assert all(['Sequence' not in str(w_.message) for w_ in w]), 'A warning was raised for Sequence.'
def train(model: Model, batch_size, ep, n_of_run=1):
train_samples = len(get_files_list(train_path_X))
valid_samples = len(get_files_list(valid_path_X))
model.fit_generator(generator=imageLoader(train_path_X, train_path_Y, batch_size=batch_size)
, validation_data=imageLoader(valid_path_X, valid_path_Y, batch_size=batch_size)
, steps_per_epoch=int(train_samples / batch_size)
, validation_steps=int(valid_samples / batch_size),
epochs=ep, callbacks=[hist])
model.save_weights(str(blocks) + 'model' + str(n_of_run) + '.h5')
# randomly shift images vertically (fraction of total height)
height_shift_range=0.1,
shear_range=0.1, # set range for random shear
zoom_range=0.2, # set range for random zoom
channel_shift_range=0., # set range for random channel shifts
# set mode for filling points outside the input boundaries
fill_mode='nearest',
cval=0., # value used for fill_mode = "constant"
horizontal_flip=True, # randomly flip images
vertical_flip=False, # randomly flip images
# set rescaling factor (applied before any other transformation)
rescale=None,
# set function that will be applied on each input
preprocessing_function=None,
# image data format, either "channels_first" or "channels_last"
data_format=None)
datagen.fit(x_train)
callbacks = [
ModelCheckpoint(
filepath=
"./teacher_models/teacher_model_epoch_{epoch:02d}-val_acc_{val_acc}.hdf5"
)
]
model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
epochs=epochs,
validation_data=(x_test, y_test),
workers=4,
callbacks=callbacks)
def test_model_methods():
a = Input(shape=(3, ), name='input_a')
b = Input(shape=(3, ), name='input_b')
a_2 = Dense(4, name='dense_1')(a)
dp = Dropout(0.5, name='dropout')
b_2 = dp(b)
model = Model([a, b], [a_2, b_2])
optimizer = 'rmsprop'
loss = 'mse'
loss_weights = [1., 0.5]
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
# training/testing doesn't work before compiling.
with pytest.raises(RuntimeError):
model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
model.compile(optimizer,
loss,
metrics=[],
loss_weights=loss_weights,
sample_weight_mode=None)
# test train_on_batch
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out = model.train_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np])
out = model.train_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
})
# test fit
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
epochs=1,
batch_size=4)
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np],
epochs=1,
batch_size=4)
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
},
epochs=1,
batch_size=4)
# test validation_split
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
epochs=1,
batch_size=4,
validation_split=0.5)
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np],
epochs=1,
batch_size=4,
validation_split=0.5)
# test validation data
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
epochs=1,
batch_size=4,
validation_data=([input_a_np,
input_b_np], [output_a_np, output_b_np]))
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np],
epochs=1,
batch_size=4,
validation_split=0.5,
validation_data=({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np]))
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
},
epochs=1,
batch_size=4,
validation_split=0.5,
validation_data=({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
}))
# test_on_batch
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out = model.test_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np])
out = model.test_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
})
# predict_on_batch
out = model.predict_on_batch([input_a_np, input_b_np])
out = model.predict_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
})
# predict, evaluate
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np],
batch_size=4)
out = model.predict([input_a_np, input_b_np], batch_size=4)
# with sample_weight
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
sample_weight = [None, np.random.random((10, ))]
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
# test accuracy metric
model.compile(optimizer, loss, metrics=['acc'], sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 5
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 5
# this should also work
model.compile(optimizer,
loss,
metrics={'dense_1': 'acc'},
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
# and this as well
model.compile(optimizer,
loss,
metrics={'dense_1': ['acc']},
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
# test starting from non-zero initial epoch
trained_epochs = []
trained_batches = []
# define tracer callback
def on_epoch_begin(epoch, logs):
trained_epochs.append(epoch)
def on_batch_begin(batch, logs):
trained_batches.append(batch)
tracker_cb = LambdaCallback(on_epoch_begin=on_epoch_begin,
on_batch_begin=on_batch_begin)
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
epochs=5,
batch_size=4,
initial_epoch=2,
callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
# test starting from non-zero initial epoch for generator too
trained_epochs = []
def gen_data(batch_sz):
while True:
yield ([
np.random.random((batch_sz, 3)),
np.random.random((batch_sz, 3))
], [
np.random.random((batch_sz, 4)),
np.random.random((batch_sz, 3))
])
out = model.fit_generator(gen_data(4),
steps_per_epoch=3,
epochs=5,
initial_epoch=2,
callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
# test with a custom metric function
def mse(y_true, y_pred):
return K.mean(K.pow(y_true - y_pred, 2))
model.compile(optimizer, loss, metrics=[mse], sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out_len = 1 + 2 * (1 + 1) # total loss + 2 outputs * (loss + metric)
assert len(out) == out_len
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == out_len
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
batch_size=4,
epochs=1)
out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np],
batch_size=4)
out = model.predict([input_a_np, input_b_np], batch_size=4)
# empty batch
with pytest.raises(ValueError):
def gen_data():
while True:
yield (np.asarray([]), np.asarray([]))
out = model.evaluate_generator(gen_data(), steps=1)
# x is not a list of numpy arrays.
with pytest.raises(ValueError):
out = model.predict([None])
# x does not match _feed_input_names.
with pytest.raises(ValueError):
out = model.predict([input_a_np, None, input_b_np])
with pytest.raises(ValueError):
out = model.predict([None, input_a_np, input_b_np])
# all input/output/weight arrays should have the same number of samples.
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np[:2]],
[output_a_np, output_b_np],
sample_weight=sample_weight)
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np[:2]],
sample_weight=sample_weight)
with pytest.raises(ValueError):
out = model.train_on_batch(
[input_a_np, input_b_np], [output_a_np, output_b_np],
sample_weight=[sample_weight[1], sample_weight[1][:2]])
# `sample_weight` is neither a dict nor a list.
with pytest.raises(TypeError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=tuple(sample_weight))
# `validation_data` is neither a tuple nor a triple.
with pytest.raises(ValueError):
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
epochs=1,
batch_size=4,
validation_data=([input_a_np, input_b_np], ))
# `loss` does not match outputs.
with pytest.raises(ValueError):
model.compile(optimizer, loss=['mse', 'mae', 'mape'])
# `loss_weights` does not match output_names.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', loss_weights={'lstm': 0.5})
# `loss_weights` does not match outputs.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', loss_weights=[0.5])
# `loss_weights` is invalid type.
with pytest.raises(TypeError):
model.compile(optimizer, loss='mse', loss_weights=(0.5, 0.5))
# `sample_weight_mode` does not match output_names.
with pytest.raises(ValueError):
model.compile(optimizer,
loss='mse',
sample_weight_mode={'lstm': 'temporal'})
# `sample_weight_mode` does not match output_names.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', sample_weight_mode=['temporal'])
# `sample_weight_mode` matches output_names partially.
with pytest.raises(ValueError):
model.compile(optimizer,
loss='mse',
sample_weight_mode={'dense_1': 'temporal'})
# `loss` does not exist.
with pytest.raises(ValueError):
model.compile(optimizer, loss=[])
model.compile(optimizer, loss=['mse', 'mae'])
model.compile(optimizer,
loss='mse',
loss_weights={
'dense_1': 0.2,
'dropout': 0.8
})
model.compile(optimizer, loss='mse', loss_weights=[0.2, 0.8])
# the rank of weight arrays should be 1.
with pytest.raises(ValueError):
out = model.train_on_batch(
[input_a_np, input_b_np], [output_a_np, output_b_np],
sample_weight=[None, np.random.random((10, 20, 30))])
model.compile(optimizer,
loss='mse',
sample_weight_mode={
'dense_1': None,
'dropout': 'temporal'
})
model.compile(optimizer, loss='mse', sample_weight_mode=[None, 'temporal'])
# the rank of output arrays should be at least 3D.
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
model.compile(optimizer,
loss,
metrics=[],
loss_weights=loss_weights,
sample_weight_mode=None)
trained_epochs = []
trained_batches = []
out = model.fit_generator(generator=RandomSequence(3),
steps_per_epoch=3,
epochs=5,
initial_epoch=0,
validation_data=RandomSequence(4),
validation_steps=3,
callbacks=[tracker_cb])
assert trained_epochs == [0, 1, 2, 3, 4]
assert trained_batches == list(range(3)) * 5
# steps_per_epoch will be equal to len of sequence if it's unspecified
trained_epochs = []
trained_batches = []
out = model.fit_generator(generator=RandomSequence(3),
epochs=5,
initial_epoch=0,
validation_data=RandomSequence(4),
callbacks=[tracker_cb])
assert trained_epochs == [0, 1, 2, 3, 4]
assert trained_batches == list(range(12)) * 5
# fit_generator will throw an exception if steps is unspecified for regular generator
with pytest.raises(ValueError):
def gen_data():
while True:
yield (np.asarray([]), np.asarray([]))
out = model.fit_generator(generator=gen_data(),
epochs=5,
initial_epoch=0,
validation_data=gen_data(),
callbacks=[tracker_cb])
# predict_generator output shape behavior should be consistent
def expected_shape(batch_size, n_batches):
return (batch_size * n_batches, 4), (batch_size * n_batches, 3)
# Multiple outputs and one step.
batch_size = 5
sequence_length = 1
shape_0, shape_1 = expected_shape(batch_size, sequence_length)
out = model.predict_generator(
RandomSequence(batch_size, sequence_length=sequence_length))
assert np.shape(out[0]) == shape_0 and np.shape(out[1]) == shape_1
# Multiple outputs and multiple steps.
batch_size = 5
sequence_length = 2
shape_0, shape_1 = expected_shape(batch_size, sequence_length)
out = model.predict_generator(
RandomSequence(batch_size, sequence_length=sequence_length))
assert np.shape(out[0]) == shape_0 and np.shape(out[1]) == shape_1
# Create a model with a single output.
single_output_model = Model([a, b], a_2)
single_output_model.compile(optimizer,
loss,
metrics=[],
sample_weight_mode=None)
# Single output and one step.
batch_size = 5
sequence_length = 1
shape_0, _ = expected_shape(batch_size, sequence_length)
out = single_output_model.predict_generator(
RandomSequence(batch_size, sequence_length=sequence_length))
assert np.shape(out) == shape_0
# Single output and multiple steps.
batch_size = 5
sequence_length = 2
shape_0, _ = expected_shape(batch_size, sequence_length)
out = single_output_model.predict_generator(
RandomSequence(batch_size, sequence_length=sequence_length))
assert np.shape(out) == shape_0
"""
datagen_for_validation = ImageDataGenerator(
zca_whitening=True, # apply ZCA whitening
zca_epsilon=1e-06 # epsilon for ZCA whitening
)
# Compute quantities required for feature-wise normalization
# (std, mean, and principal components if ZCA whitening is applied).
datagen_for_validation.fit(x_test)
"""
datagen.fit(x_train)
# Fit the model on the batches generated by datagen.flow().
model.fit_generator(datagen.flow(x_train, y_train, batch_size=batch_size),
epochs=epochs,
callbacks=[lr_cb],
validation_data=(x_test, y_test, batch_size),
validation_steps=100,
verbose=1)
# Save model and weights
if not os.path.isdir(save_dir):
os.makedirs(save_dir)
model_path = os.path.join(save_dir, model_name)
model.save(model_path)
print('Saved trained model at %s ' % model_path)
# Score trained model.
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
class TL(Model):
"""
Triplet-Loss trained Neural Network.
https://arxiv.org/abs/1503.03832
"""
def __init__(self, base=None, siamese=None):
super(TL, self).__init__()
# Store the base model.
assert (base != None)
self.base = base
# For loading.
if base != None and siamese != None:
self.base = base
self.siamese = siamese
self.latent_dim = self.base.outputs[0].shape[1]
return
# Get the latent dimension.
assert len(self.base.outputs) == 1
assert len(self.base.outputs[0].shape) == 2
self.latent_dim = self.base.outputs[0].shape[1]
# Get the input shape.
input_shape = self.base.inputs[0].shape.as_list()[1:]
# Create the anchor.
input_anchor = layers.Input(shape=input_shape)
output_anchor = input_anchor
output_anchor = self.base(output_anchor)
# Create the positive.
input_positive = layers.Input(shape=input_shape)
output_positive = input_positive
output_positive = self.base(output_positive)
# Create the negative.
input_negative = layers.Input(shape=input_shape)
output_negative = input_negative
output_negative = self.base(output_negative)
# Create a dummy output.
output = layers.concatenate(
[output_anchor, output_positive, output_negative])
# Create the model.
self.siamese = Model([input_anchor, input_positive, input_negative],
output,
name="triplet_model")
def compile(self,
optimizer,
loss=None,
metrics=None,
loss_weights=None,
sample_weight_mode=None,
weighted_metrics=None,
target_tensors=None,
triplet_loss="mse",
**kwargs):
"""
Compiles the TL.
Additionally to the default functionality of *compile*, it adds the triplet-loss.
In order to do so you have to provide it via the parameter *triplet_loss*.
The VAE loss is similar to
>>> vae_loss = max(0.0, pos_dist - neg_dist + alpha)
See the literature for details.
Additional args:
triplet_loss (string): The base-loss for the triplet-loss. Values are either *euclidean* for euclidean norm or *cosine* for cosine similarity.
"""
assert loss == None, "Not expected to provide an explicit loss for TL. Use 'triplet_loss'"
self.triplet_loss = triplet_loss
def triplet_loss_function(y_true, y_pred, alpha=0.4):
anchor = y_pred[:, 0:self.latent_dim]
positive = y_pred[:, self.latent_dim:self.latent_dim * 2]
negative = y_pred[:, self.latent_dim * 2:self.latent_dim * 3]
if triplet_loss == "euclidean":
pos_dist = euclidean_loss(positive, anchor)
neg_dist = euclidean_loss(negative, anchor)
elif triplet_loss == "cosine":
pos_dist = cosine_loss(positive, anchor)
neg_dist = cosine_loss(negative, anchor)
else:
raise Exception("Unexpected: " + triplet_loss)
basic_loss = pos_dist - neg_dist + alpha
loss = K.maximum(basic_loss, 0.0)
return loss
loss = triplet_loss_function
self.siamese.compile(optimizer, loss, metrics, loss_weights,
sample_weight_mode, weighted_metrics, **kwargs)
def fit(self,
x=None,
y=None,
batch_size=None,
minibatch_size=None,
epochs=1,
verbose=1,
callbacks=None,
validation_split=0.,
validation_data=None,
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
**kwargs):
"""
This is basically the same as in vanilla Keras.
Additional args:
minibatch_size (int): The model internally does some sampling. The *minibatch_size* specifies how many candidates to use in order to create a triplet for training.
"""
assert minibatch_size != None, "ERROR! Must provide 'minibatch_size'."
assert steps_per_epoch != None, "ERROR! Must provide 'steps_per_epoch'."
assert validation_steps != None, "ERROR! Must provide 'validation_steps'."
y_dummy = np.zeros((batch_size, self.latent_dim * 3))
# Template generator.
def triplet_loss_generator(x_generator, y_generator, model, sampling):
# Get the classes.
classes = sorted(list(set(y_generator)))
# Sort by classes for easy indexing.
class_indices = {}
for c in classes:
class_indices[c] = []
for index, c in enumerate(y_generator):
class_indices[c].append(index)
# Compute the complements.
class_complements = {}
for c in classes:
class_complements[c] = [c2 for c2 in classes if c2 != c]
# Generator loop.
while True:
x_input_anchors = []
x_input_positives = []
x_input_negatives = []
# Generate a whole batch.
for _ in range(batch_size):
anchor_class = random.choice(classes)
anchor_index = random.choice(class_indices[anchor_class])
anchor_input = x_generator[anchor_index]
#print("anchor_class", anchor_class)
anchor_latent = self.base.predict(
np.expand_dims(anchor_input, axis=0))[0]
# Generate some positive candidates.
positive_candidates = []
while len(positive_candidates) < minibatch_size:
positive_class = anchor_class
positive_index = random.choice(
class_indices[positive_class])
positive_input = x_generator[positive_index]
assert positive_class == y_generator[positive_index]
#print("positive_class", positive_class)
positive_candidates.append(positive_input)
# Find the farthest positive candidate.
positive_candidates = np.array(positive_candidates)
positive_latents = self.base.predict(positive_candidates)
positive_extremum = compute_latent_extremum(
anchor_latent, positive_latents, "argmax",
self.triplet_loss)
positive_input = positive_candidates[positive_extremum]
# Generate some negative candidates.
negative_candidates = []
while len(negative_candidates) < minibatch_size:
negative_class = random.choice(
class_complements[anchor_class])
negative_index = random.choice(
class_indices[negative_class])
negative_input = x_generator[negative_index]
assert negative_class == y_generator[negative_index]
#print("negative_class", negative_class)
negative_candidates.append(negative_input)
# Find the closest negative candidate.
negative_candidates = np.array(negative_candidates)
negative_latents = self.base.predict(negative_candidates)
negative_extremum = compute_latent_extremum(
anchor_latent, negative_latents, "argmin",
self.triplet_loss)
negative_input = negative_candidates[negative_extremum]
# Done.
x_input_anchors.append(anchor_input)
x_input_positives.append(positive_input)
x_input_negatives.append(negative_input)
x_input_anchors = np.array(x_input_anchors)
x_input_positives = np.array(x_input_positives)
x_input_negatives = np.array(x_input_negatives)
x_input = [
x_input_anchors, x_input_positives, x_input_negatives
]
yield x_input, y_dummy
# Create the generators.
training_generator = triplet_loss_generator(x, y, batch_size,
self.siamese)
if validation_data != None:
validation_generator = triplet_loss_generator(
validation_data[0], validation_data[1], batch_size,
self.siamese)
else:
validation_generator = None
# Create the history.
history_keys = ["loss", "val_loss"]
history = {}
for history_key in history_keys:
history[history_key] = []
# Training the model
for epoch in range(epochs):
print("Epoch " + str(epoch + 1) + "/" + str(epochs) + "...")
# Generating data for training.
training_input, training_output = next(training_generator)
if validation_generator != None:
validation_input, validation_output = next(
validation_generator)
model_history = self.siamese.fit(
training_input,
training_output,
validation_data=(validation_input, validation_output),
epochs=1,
steps_per_epoch=steps_per_epoch,
verbose=0,
validation_steps=validation_steps)
# Update the history.
for history_key in history_keys:
history_value = model_history.history[history_key]
history[history_key].append(history_value)
print(history_key, history_value)
return history
def fit_generator(self,
generator,
steps_per_epoch=None,
epochs=1,
verbose=1,
callbacks=None,
validation_data=None,
validation_steps=None,
class_weight=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
shuffle=True,
initial_epoch=0):
"""
Coming soon...
"""
print("TODO: implement fit_generator!")
raise Exception("Not implemented!")
return self.siamese.fit_generator(generator, steps_per_epoch, epochs,
verbose, callbacks, validation_data,
validation_steps, class_weight,
max_queue_size, workers,
use_multiprocessing, shuffle,
initial_epoch)
def evaluate(self,
x=None,
y=None,
batch_size=None,
verbose=1,
sample_weight=None,
steps=None):
"""
Evaluates the model. Same as vanilla Keras.
"""
return self.siamese.evaluate(x,
y,
batch_size,
verbose,
sample_weight,
steps=None)
def predict(self, x, batch_size=None, verbose=0, steps=None):
"""
Does a prediction. Same as vanilla Keras.
"""
return self.siamese.predict(x, batch_size, verbose, steps)
def summary(self):
"""
Provides a summary.
"""
print("Basemodel:")
self.base.summary()
print("Siamese model:")
self.siamese.summary()
def save(self, path):
"""
Saves the TL.
This includes the whole Siamese Net plus the base-model.
This code
>>> tl.save("myae.h5")
will create the files *tl.h5*, and *tl-base.h5*.
"""
self.siamese.save(path)
self.base.save(append_to_filepath(path, "-base"))
y = TimeDistributed(Conv2D(512, 3, padding='same',
activation='relu'))(y)
y = TimeDistributed(Conv2D(512, 3, padding='same',
activation='relu'))(y)
y = TimeDistributed(MaxPooling2D(pool_size=(2, 2)))(y)
# Tensor shape: (B, T, 4, 4, 512) -> (B, T, 1, 512)
y = TimeDistributed(GlobalAveragePooling2D())(y)
with tf.device(available_devices[3]):
y = LSTM(128, return_sequences=False, return_state=False)(y)
prediction = Dense(num_classes, activation='softmax')(y)
# Instantiate a 'keras.engine.training.Model' instance
model = Model(inputs=cnn_input, outputs=prediction)
# Compile model
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(lr=learning_rate),
metrics=['accuracy'])
# Train model
datadir = 'Z:/1. 프로젝트/2018_삼성SDS_스타크래프트/Supervised/trainingData_v3/data(선수별)/박성균/128/'
generator = generate_arrays_from_directory(datadir, batch_size)
steps_per_epoch = math.ceil(os.listdir(datadir).__len__() / batch_size)
model.fit_generator(generator=generator,
steps_per_epoch=steps_per_epoch,
epochs=epochs,
max_queue_size=10,
verbose=1)
def test_model_methods():
a = Input(shape=(3,), name='input_a')
b = Input(shape=(3,), name='input_b')
a_2 = Dense(4, name='dense_1')(a)
dp = Dropout(0.5, name='dropout')
b_2 = dp(b)
model = Model([a, b], [a_2, b_2])
optimizer = 'rmsprop'
loss = 'mse'
loss_weights = [1., 0.5]
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
input_a_df = pd.DataFrame(input_a_np)
input_b_df = pd.DataFrame(input_b_np)
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
output_a_df = pd.DataFrame(output_a_np)
output_b_df = pd.DataFrame(output_b_np)
# training/testing doesn't work before compiling.
with pytest.raises(RuntimeError):
model.train_on_batch([input_a_np, input_b_np], [output_a_np, output_b_np])
model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
sample_weight_mode=None)
# test train_on_batch
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out = model.train_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np])
out = model.train_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np})
out = model.train_on_batch([input_a_df, input_b_df],
[output_a_df, output_b_df])
# test fit
out = model.fit([input_a_np, input_b_np],
[output_a_np, output_b_np], epochs=1, batch_size=4)
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np], epochs=1, batch_size=4)
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np},
epochs=1, batch_size=4)
out = model.fit([input_a_df, input_b_df],
[output_a_df, output_b_df], epochs=1, batch_size=4)
# test validation_split
out = model.fit([input_a_np, input_b_np],
[output_a_np, output_b_np],
epochs=1, batch_size=4, validation_split=0.5)
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np],
epochs=1, batch_size=4, validation_split=0.5)
# test validation data
out = model.fit([input_a_np, input_b_np],
[output_a_np, output_b_np],
epochs=1, batch_size=4,
validation_data=([input_a_np, input_b_np], [output_a_np, output_b_np]))
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np],
epochs=1, batch_size=4, validation_split=0.5,
validation_data=({'input_a': input_a_np, 'input_b': input_b_np}, [output_a_np, output_b_np]))
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np},
epochs=1, batch_size=4, validation_split=0.5,
validation_data=(
{'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np}))
# test_on_batch
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out = model.test_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np])
out = model.test_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np})
out = model.test_on_batch([input_a_df, input_b_df],
[output_a_df, output_b_df])
# predict_on_batch
out = model.predict_on_batch([input_a_np, input_b_np])
out = model.predict_on_batch({'input_a': input_a_np, 'input_b': input_b_np})
out = model.predict_on_batch([input_a_df, input_b_df])
# predict, evaluate
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4)
out = model.evaluate([input_a_df, input_b_df], [output_a_df, output_b_df], batch_size=4)
out = model.predict([input_a_np, input_b_np], batch_size=4)
out = model.predict([input_a_df, input_b_df], batch_size=4)
# with sample_weight
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
sample_weight = [None, np.random.random((10,))]
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
# test accuracy metric
model.compile(optimizer, loss, metrics=['acc'],
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 5
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 5
# this should also work
model.compile(optimizer, loss, metrics={'dense_1': 'acc'},
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
# and this as well
model.compile(optimizer, loss, metrics={'dense_1': ['acc']},
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
# test starting from non-zero initial epoch
trained_epochs = []
# define tracer callback
def on_epoch_begin(epoch, logs):
trained_epochs.append(epoch)
tracker_cb = LambdaCallback(on_epoch_begin=on_epoch_begin)
out = model.fit([input_a_np, input_b_np],
[output_a_np, output_b_np], epochs=5, batch_size=4,
initial_epoch=2, callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
# test starting from non-zero initial epoch for generator too
trained_epochs = []
def gen_data(batch_sz):
while True:
yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
[np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])
out = model.fit_generator(gen_data(4), steps_per_epoch=3, epochs=5,
initial_epoch=2, callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
# test with a custom metric function
def mse(y_true, y_pred):
return K.mean(K.pow(y_true - y_pred, 2))
model.compile(optimizer, loss, metrics=[mse],
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out_len = 1 + 2 * (1 + 1) # total loss + 2 outputs * (loss + metric)
assert len(out) == out_len
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == out_len
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4, epochs=1)
out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4)
out = model.predict([input_a_np, input_b_np], batch_size=4)
# empty batch
with pytest.raises(ValueError):
def gen_data():
while True:
yield (np.asarray([]), np.asarray([]))
out = model.evaluate_generator(gen_data(), steps=1)
# x is not a list of numpy arrays.
with pytest.raises(ValueError):
out = model.predict([None])
# x does not match _feed_input_names.
with pytest.raises(ValueError):
out = model.predict([input_a_np, None, input_b_np])
with pytest.raises(ValueError):
out = model.predict([None, input_a_np, input_b_np])
# all input/output/weight arrays should have the same number of samples.
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np[:2]],
[output_a_np, output_b_np],
sample_weight=sample_weight)
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np[:2]],
sample_weight=sample_weight)
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=[sample_weight[1], sample_weight[1][:2]])
# `sample_weight` is neither a dict nor a list.
with pytest.raises(TypeError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=tuple(sample_weight))
# `validation_data` is neither a tuple nor a triple.
with pytest.raises(ValueError):
out = model.fit([input_a_np, input_b_np],
[output_a_np, output_b_np],
epochs=1, batch_size=4,
validation_data=([input_a_np, input_b_np],))
# `loss` does not match outputs.
with pytest.raises(ValueError):
model.compile(optimizer, loss=['mse', 'mae', 'mape'])
# `loss_weights` does not match output_names.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', loss_weights={'lstm': 0.5})
# `loss_weights` does not match outputs.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', loss_weights=[0.5])
# `loss_weights` is invalid type.
with pytest.raises(TypeError):
model.compile(optimizer, loss='mse', loss_weights=(0.5, 0.5))
# `sample_weight_mode` does not match output_names.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', sample_weight_mode={'lstm': 'temporal'})
# `sample_weight_mode` does not match output_names.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', sample_weight_mode=['temporal'])
# `sample_weight_mode` matches output_names partially.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', sample_weight_mode={'dense_1': 'temporal'})
# `loss` does not exist.
with pytest.raises(ValueError):
model.compile(optimizer, loss=[])
model.compile(optimizer, loss=['mse', 'mae'])
model.compile(optimizer, loss='mse', loss_weights={'dense_1': 0.2, 'dropout': 0.8})
model.compile(optimizer, loss='mse', loss_weights=[0.2, 0.8])
# the rank of weight arrays should be 1.
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=[None, np.random.random((10, 20, 30))])
model.compile(optimizer, loss='mse', sample_weight_mode={'dense_1': None, 'dropout': 'temporal'})
model.compile(optimizer, loss='mse', sample_weight_mode=[None, 'temporal'])
# the rank of output arrays should be at least 3D.
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
sample_weight_mode=None)
trained_epochs = []
out = model.fit_generator(generator=RandomSequence(3), steps_per_epoch=12, epochs=5,
initial_epoch=0, validation_data=RandomSequence(4),
validation_steps=12, callbacks=[tracker_cb])
assert trained_epochs == [0, 1, 2, 3, 4]
def test_model_methods():
a = Input(shape=(3,), name='input_a')
b = Input(shape=(3,), name='input_b')
a_2 = Dense(4, name='dense_1')(a)
dp = Dropout(0.5, name='dropout')
b_2 = dp(b)
model = Model([a, b], [a_2, b_2])
optimizer = 'rmsprop'
loss = 'mse'
loss_weights = [1., 0.5]
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
# training/testing doesn't work before compiling.
with pytest.raises(RuntimeError):
model.train_on_batch([input_a_np, input_b_np], [output_a_np, output_b_np])
model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
sample_weight_mode=None)
# test train_on_batch
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out = model.train_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np])
out = model.train_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np})
# test fit
out = model.fit([input_a_np, input_b_np],
[output_a_np, output_b_np], epochs=1, batch_size=4)
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np], epochs=1, batch_size=4)
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np},
epochs=1, batch_size=4)
# test validation_split
out = model.fit([input_a_np, input_b_np],
[output_a_np, output_b_np],
epochs=1, batch_size=4, validation_split=0.5)
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np],
epochs=1, batch_size=4, validation_split=0.5)
# test validation data
out = model.fit([input_a_np, input_b_np],
[output_a_np, output_b_np],
epochs=1, batch_size=4,
validation_data=([input_a_np, input_b_np], [output_a_np, output_b_np]))
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np],
epochs=1, batch_size=4, validation_split=0.5,
validation_data=({'input_a': input_a_np, 'input_b': input_b_np}, [output_a_np, output_b_np]))
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np},
epochs=1, batch_size=4, validation_split=0.5,
validation_data=(
{'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np}))
# test_on_batch
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out = model.test_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np])
out = model.test_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np})
# predict_on_batch
out = model.predict_on_batch([input_a_np, input_b_np])
out = model.predict_on_batch({'input_a': input_a_np, 'input_b': input_b_np})
# predict, evaluate
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4)
out = model.predict([input_a_np, input_b_np], batch_size=4)
# with sample_weight
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
sample_weight = [None, np.random.random((10,))]
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
# test accuracy metric
model.compile(optimizer, loss, metrics=['acc'],
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 5
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 5
# this should also work
model.compile(optimizer, loss, metrics={'dense_1': 'acc'},
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
# and this as well
model.compile(optimizer, loss, metrics={'dense_1': ['acc']},
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
# test starting from non-zero initial epoch
trained_epochs = []
trained_batches = []
# define tracer callback
def on_epoch_begin(epoch, logs):
trained_epochs.append(epoch)
def on_batch_begin(batch, logs):
trained_batches.append(batch)
tracker_cb = LambdaCallback(on_epoch_begin=on_epoch_begin,
on_batch_begin=on_batch_begin)
out = model.fit([input_a_np, input_b_np],
[output_a_np, output_b_np], epochs=5, batch_size=4,
initial_epoch=2, callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
# test starting from non-zero initial epoch for generator too
trained_epochs = []
def gen_data(batch_sz):
while True:
yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
[np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])
out = model.fit_generator(gen_data(4), steps_per_epoch=3, epochs=5,
initial_epoch=2, callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
# test with a custom metric function
def mse(y_true, y_pred):
return K.mean(K.pow(y_true - y_pred, 2))
model.compile(optimizer, loss, metrics=[mse],
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out_len = 1 + 2 * (1 + 1) # total loss + 2 outputs * (loss + metric)
assert len(out) == out_len
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == out_len
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4, epochs=1)
out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4)
out = model.predict([input_a_np, input_b_np], batch_size=4)
# enable verbose for evaluate_generator
out = model.evaluate_generator(gen_data(4), steps=3, verbose=1)
# empty batch
with pytest.raises(ValueError):
def gen_data():
while True:
yield (np.asarray([]), np.asarray([]))
out = model.evaluate_generator(gen_data(), steps=1)
# x is not a list of numpy arrays.
with pytest.raises(ValueError):
out = model.predict([None])
# x does not match _feed_input_names.
with pytest.raises(ValueError):
out = model.predict([input_a_np, None, input_b_np])
with pytest.raises(ValueError):
out = model.predict([None, input_a_np, input_b_np])
# all input/output/weight arrays should have the same number of samples.
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np[:2]],
[output_a_np, output_b_np],
sample_weight=sample_weight)
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np[:2]],
sample_weight=sample_weight)
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=[sample_weight[1], sample_weight[1][:2]])
# `sample_weight` is neither a dict nor a list.
with pytest.raises(TypeError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=tuple(sample_weight))
# `validation_data` is neither a tuple nor a triple.
with pytest.raises(ValueError):
out = model.fit([input_a_np, input_b_np],
[output_a_np, output_b_np],
epochs=1, batch_size=4,
validation_data=([input_a_np, input_b_np],))
# `loss` does not match outputs.
with pytest.raises(ValueError):
model.compile(optimizer, loss=['mse', 'mae', 'mape'])
# `loss_weights` does not match output_names.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', loss_weights={'lstm': 0.5})
# `loss_weights` does not match outputs.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', loss_weights=[0.5])
# `loss_weights` is invalid type.
with pytest.raises(TypeError):
model.compile(optimizer, loss='mse', loss_weights=(0.5, 0.5))
# `sample_weight_mode` does not match output_names.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', sample_weight_mode={'lstm': 'temporal'})
# `sample_weight_mode` does not match output_names.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', sample_weight_mode=['temporal'])
# `sample_weight_mode` matches output_names partially.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', sample_weight_mode={'dense_1': 'temporal'})
# `loss` does not exist.
with pytest.raises(ValueError):
model.compile(optimizer, loss=[])
model.compile(optimizer, loss=['mse', 'mae'])
model.compile(optimizer, loss='mse', loss_weights={'dense_1': 0.2, 'dropout': 0.8})
model.compile(optimizer, loss='mse', loss_weights=[0.2, 0.8])
# the rank of weight arrays should be 1.
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=[None, np.random.random((10, 20, 30))])
model.compile(optimizer, loss='mse', sample_weight_mode={'dense_1': None, 'dropout': 'temporal'})
model.compile(optimizer, loss='mse', sample_weight_mode=[None, 'temporal'])
# the rank of output arrays should be at least 3D.
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
sample_weight_mode=None)
trained_epochs = []
trained_batches = []
out = model.fit_generator(generator=RandomSequence(3), steps_per_epoch=3, epochs=5,
initial_epoch=0, validation_data=RandomSequence(4),
validation_steps=3, callbacks=[tracker_cb])
assert trained_epochs == [0, 1, 2, 3, 4]
assert trained_batches == list(range(3)) * 5
# steps_per_epoch will be equal to len of sequence if it's unspecified
trained_epochs = []
trained_batches = []
out = model.fit_generator(generator=RandomSequence(3), epochs=5,
initial_epoch=0, validation_data=RandomSequence(4),
callbacks=[tracker_cb])
assert trained_epochs == [0, 1, 2, 3, 4]
assert trained_batches == list(range(12)) * 5
# fit_generator will throw an exception if steps is unspecified for regular generator
with pytest.raises(ValueError):
def gen_data():
while True:
yield (np.asarray([]), np.asarray([]))
out = model.fit_generator(generator=gen_data(), epochs=5,
initial_epoch=0, validation_data=gen_data(),
callbacks=[tracker_cb])
# Check if generator is only accessed an expected number of times
gen_counters = [0, 0]
def gen_data(i):
while True:
gen_counters[i] += 1
yield ([np.random.random((1, 3)), np.random.random((1, 3))],
[np.random.random((1, 4)), np.random.random((1, 3))])
out = model.fit_generator(generator=gen_data(0), epochs=3,
steps_per_epoch=2,
validation_data=gen_data(1),
validation_steps=1,
max_queue_size=2,
workers=2)
# Need range check here as filling of the queue depends on sleep in the enqueuers
assert 6 <= gen_counters[0] <= 8
# 12 = (epoch * workers * validation steps * max_queue_size)
assert 3 <= gen_counters[1] <= 12
gen_counters = [0]
out = model.fit_generator(generator=RandomSequence(3), epochs=3,
validation_data=gen_data(0),
validation_steps=1,
max_queue_size=2,
workers=2)
# 12 = (epoch * workers * validation steps * max_queue_size)
# Need range check here as filling of the queue depends on sleep in the enqueuers
assert 3 <= gen_counters[0] <= 12
# predict_generator output shape behavior should be consistent
def expected_shape(batch_size, n_batches):
return (batch_size * n_batches, 4), (batch_size * n_batches, 3)
# Multiple outputs and one step.
batch_size = 5
sequence_length = 1
shape_0, shape_1 = expected_shape(batch_size, sequence_length)
out = model.predict_generator(RandomSequence(batch_size,
sequence_length=sequence_length))
assert np.shape(out[0]) == shape_0 and np.shape(out[1]) == shape_1
# Multiple outputs and multiple steps.
batch_size = 5
sequence_length = 2
shape_0, shape_1 = expected_shape(batch_size, sequence_length)
out = model.predict_generator(RandomSequence(batch_size,
sequence_length=sequence_length))
assert np.shape(out[0]) == shape_0 and np.shape(out[1]) == shape_1
# Create a model with a single output.
single_output_model = Model([a, b], a_2)
single_output_model.compile(optimizer, loss, metrics=[], sample_weight_mode=None)
# Single output and one step.
batch_size = 5
sequence_length = 1
shape_0, _ = expected_shape(batch_size, sequence_length)
out = single_output_model.predict_generator(RandomSequence(batch_size,
sequence_length=sequence_length))
assert np.shape(out) == shape_0
# Single output and multiple steps.
batch_size = 5
sequence_length = 2
shape_0, _ = expected_shape(batch_size, sequence_length)
out = single_output_model.predict_generator(RandomSequence(batch_size,
sequence_length=sequence_length))
assert np.shape(out) == shape_0
def test_model_with_external_loss():
# None loss, only regularization loss.
a = Input(shape=(3,), name='input_a')
a_2 = Dense(4, name='dense_1',
kernel_regularizer='l1',
bias_regularizer='l2')(a)
dp = Dropout(0.5, name='dropout')
a_3 = dp(a_2)
model = Model(a, [a_2, a_3])
optimizer = 'rmsprop'
loss = None
model.compile(optimizer, loss, metrics=['mae'])
input_a_np = np.random.random((10, 3))
# test train_on_batch
out = model.train_on_batch(input_a_np, None)
out = model.test_on_batch(input_a_np, None)
# fit
out = model.fit(input_a_np, None)
# evaluate
out = model.evaluate(input_a_np, None)
# No dropout, external loss.
a = Input(shape=(3,), name='input_a')
a_2 = Dense(4, name='dense_1')(a)
a_3 = Dense(4, name='dense_2')(a)
model = Model(a, [a_2, a_3])
model.add_loss(K.mean(a_3 + a_2))
optimizer = 'rmsprop'
loss = None
model.compile(optimizer, loss, metrics=['mae'])
# test train_on_batch
out = model.train_on_batch(input_a_np, None)
out = model.test_on_batch(input_a_np, None)
# fit
out = model.fit(input_a_np, None)
# evaluate
out = model.evaluate(input_a_np, None)
# Test fit with no external data at all.
if K.backend() == 'tensorflow':
import tensorflow as tf
a = Input(tensor=tf.Variable(input_a_np, dtype=tf.float32))
a_2 = Dense(4, name='dense_1')(a)
a_2 = Dropout(0.5, name='dropout')(a_2)
model = Model(a, a_2)
model.add_loss(K.mean(a_2))
model.compile(optimizer='rmsprop',
loss=None,
metrics=['mean_squared_error'])
# test train_on_batch
out = model.train_on_batch(None, None)
out = model.test_on_batch(None, None)
out = model.predict_on_batch(None)
# test fit
with pytest.raises(ValueError):
out = model.fit(None, None, epochs=1, batch_size=10)
out = model.fit(None, None, epochs=1, steps_per_epoch=1)
# define a generator to produce x=None and y=None
def data_tensors_generator():
while True:
yield (None, None)
generator = data_tensors_generator()
# test fit_generator for framework-native data tensors
out = model.fit_generator(generator, epochs=1,
steps_per_epoch=3)
# test evaluate_generator for framework-native data tensors
out = model.evaluate_generator(generator, steps=3)
# test fit with validation data
with pytest.raises(ValueError):
out = model.fit(None, None,
epochs=1,
steps_per_epoch=None,
validation_steps=2)
out = model.fit(None, None,
epochs=1,
steps_per_epoch=2,
validation_steps=2)
# test evaluate
with pytest.raises(ValueError):
out = model.evaluate(None, None, batch_size=10)
out = model.evaluate(None, None, steps=3)
# test predict
with pytest.raises(ValueError):
out = model.predict(None, batch_size=10)
out = model.predict(None, steps=3)
assert out.shape == (10 * 3, 4)
# Test multi-output model without external data.
a = Input(tensor=tf.Variable(input_a_np, dtype=tf.float32))
a_1 = Dense(4, name='dense_1')(a)
a_2 = Dropout(0.5, name='dropout')(a_1)
model = Model(a, [a_1, a_2])
model.add_loss(K.mean(a_2))
model.compile(optimizer='rmsprop',
loss=None,
metrics=['mean_squared_error'])
# test train_on_batch
out = model.train_on_batch(None, None)
out = model.test_on_batch(None, None)
out = model.predict_on_batch(None)
# test fit
with pytest.raises(ValueError):
out = model.fit(None, None, epochs=1, batch_size=10)
out = model.fit(None, None, epochs=1, steps_per_epoch=1)
# test fit with validation data
with pytest.raises(ValueError):
out = model.fit(None, None,
epochs=1,
steps_per_epoch=None,
validation_steps=2)
out = model.fit(None, None,
epochs=1,
steps_per_epoch=2,
validation_steps=2)
# test evaluate
with pytest.raises(ValueError):
out = model.evaluate(None, None, batch_size=10)
out = model.evaluate(None, None, steps=3)
# test predict
with pytest.raises(ValueError):
out = model.predict(None, batch_size=10)
out = model.predict(None, steps=3)
assert len(out) == 2
assert out[0].shape == (10 * 3, 4)
assert out[1].shape == (10 * 3, 4)
class ResiCNN: # My JackNet with residual block
cnn_filter_num = 64
cnn_kernel_size = 3
def __init__(self, channels=3):
self.model = None
self.optimizer = None
self.channels = channels
def bulid(self): # build model
image_in = Input((None, None, self.channels))
conv = Conv2D(filters=self.cnn_filter_num,
kernel_size=self.cnn_kernel_size,
strides=(1, 1),
padding='same',
data_format='channels_last')(image_in)
conv = Activation('relu')(conv)
x = conv
for layers in range(8):
x = self._build_residual_block(x)
conv_out = Conv2D(filters=self.channels,
kernel_size=self.cnn_kernel_size,
strides=(1, 1),
padding='same',
data_format='channels_last')(x)
output = Add()([image_in, conv_out])
self.model = Model(image_in, output, name='model')
def _build_residual_block(self, x): # build residual block
x_in = x
x = Conv2D(filters=self.cnn_filter_num,
kernel_size=self.cnn_kernel_size,
strides=(1, 1),
padding='same',
data_format='channels_last')(x)
x = BatchNormalization(axis=-1)(x)
x = Activation('relu')(x)
x = Conv2D(filters=self.cnn_filter_num,
kernel_size=self.cnn_kernel_size,
strides=(1, 1),
padding='same',
data_format='channels_last')(x)
x = BatchNormalization(axis=-1)(x)
x = Add()([x_in, x])
x = Activation("relu")(x)
return x
def predict(self, x): # denoise on input x
if x.ndim == 3:
x = x.reshape(1, x.shape[0], x.shape[1], self.channels)
return self.model.predict_on_batch(x)
def load(self, config_path, model_path): # load model
print('restore model...')
if os.path.exists(config_path) and os.path.exists(model_path):
with open(config_path, 'r') as fp:
self.model = Model.from_config(json.load(fp))
self.model.load_weights(model_path)
return True
return False
def save(self, config_path, model_path): # save model
with open(config_path, 'w') as fp:
json.dump(self.model.get_config(), fp)
self.model.save_weights(model_path)
def compile(self): # choose adam optimizer and set learning rate
self.optimizer = Adam(lr=1e-2)
self.model.compile(optimizer=self.optimizer, loss=self.loss)
def train_generator(self,
data,
epochs=1,
steps_per_epochs=None,
callbacks=None):
self.model.fit_generator(iter(data),
epochs=epochs,
steps_per_epoch=steps_per_epochs,
callbacks=callbacks)
def train(self, data, epochs=1, callbacks=None):
self.model.fit(x=data[0],
y=data[1],
epochs=epochs,
batch_size=8,
callbacks=callbacks)
@staticmethod
def loss(y_true, y_pred): # loss function, mean square error
return 0.5 * K.sum(K.square(y_pred - y_true), axis=-1)
W_regularizer=l2(.0005),
activation='softmax')(x)
model = Model(base_model.input, predictions)
# model = load_model(filepath='./model4.29-0.69.hdf5')
opt = SGD(lr=.01, momentum=.9)
model.compile(loss='categorical_crossentropy',
optimizer=opt,
metrics=['accuracy'])
model_json = model.to_json()
with open("{}/{}.def".format(job_path, model_name), "w") as json_file:
json_file.write(model_json)
print(train_generator.class_indices)
f = open("{}/{}.cls".format(job_path, model_name), 'w')
f.write(json.dumps(train_generator.class_indices))
f.close()
# fine-tune the model
model.fit_generator(
train_generator,
samples_per_epoch=nb_train_samples,
epochs=epochs,
validation_data=validation_generator,
nb_val_samples=nb_validation_samples,
callbacks=[lr_scheduler, csv_logger, checkpointer, tensorboard])
model.save_weights("{}/final_{}_{}.h5".format(job_path, job_name, model_name))
def test_model_methods():
a = Input(shape=(3, ), name='input_a')
b = Input(shape=(3, ), name='input_b')
a_2 = Dense(4, name='dense_1')(a)
dp = Dropout(0.5, name='dropout')
b_2 = dp(b)
model = Model([a, b], [a_2, b_2])
optimizer = 'rmsprop'
loss = 'mse'
loss_weights = [1., 0.5]
model.compile(optimizer,
loss,
metrics=[],
loss_weights=loss_weights,
sample_weight_mode=None)
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
# test train_on_batch
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out = model.train_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np])
out = model.train_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
})
# test fit
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
nb_epoch=1,
batch_size=4)
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np],
nb_epoch=1,
batch_size=4)
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
},
nb_epoch=1,
batch_size=4)
# test validation_split
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
nb_epoch=1,
batch_size=4,
validation_split=0.5)
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np],
nb_epoch=1,
batch_size=4,
validation_split=0.5)
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
},
nb_epoch=1,
batch_size=4,
validation_split=0.5)
# test validation data
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
nb_epoch=1,
batch_size=4,
validation_data=([input_a_np,
input_b_np], [output_a_np, output_b_np]))
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np],
nb_epoch=1,
batch_size=4,
validation_split=0.5,
validation_data=({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np]))
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
},
nb_epoch=1,
batch_size=4,
validation_split=0.5,
validation_data=({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
}))
# test_on_batch
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out = model.test_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np])
out = model.test_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
})
# predict_on_batch
out = model.predict_on_batch([input_a_np, input_b_np])
out = model.predict_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
})
# predict, evaluate
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np],
batch_size=4)
out = model.predict([input_a_np, input_b_np], batch_size=4)
# with sample_weight
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
sample_weight = [None, np.random.random((10, ))]
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
# test accuracy metric
model.compile(optimizer, loss, metrics=['acc'], sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 5
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 5
# this should also work
model.compile(optimizer,
loss,
metrics={'dense_1': 'acc'},
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
# and this as well
model.compile(optimizer,
loss,
metrics={'dense_1': ['acc']},
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
# test starting from non-zero initial epoch
trained_epochs = []
def on_epoch_begin(epoch, logs):
trained_epochs.append(epoch)
tracker_cb = LambdaCallback(on_epoch_begin=on_epoch_begin)
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
nb_epoch=5,
batch_size=4,
initial_epoch=2,
callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
# test starting from non-zero initial epoch for generator too
trained_epochs = []
def gen_data(batch_sz):
while True:
yield ([
np.random.random((batch_sz, 3)),
np.random.random((batch_sz, 3))
], [
np.random.random((batch_sz, 4)),
np.random.random((batch_sz, 3))
])
out = model.fit_generator(gen_data(4),
samples_per_epoch=10,
nb_epoch=5,
initial_epoch=2,
callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
# test with a custom metric function
mse = lambda y_true, y_pred: K.mean(K.pow(y_true - y_pred, 2))
def mse_powers(y_true, y_pred):
m = mse(y_true, y_pred)
return {'mse_squared': K.pow(m, 2), 'mse_cubed': K.pow(m, 3)}
model.compile(optimizer,
loss,
metrics=[mse, mse_powers],
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out_len = 1 + 2 * 4 # total loss, per layer: loss + 3 metrics
assert len(out) == out_len
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == out_len
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
batch_size=4,
nb_epoch=1)
out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np],
batch_size=4)
out = model.predict([input_a_np, input_b_np], batch_size=4)
def test_model_with_external_loss():
# None loss, only regularization loss.
a = Input(shape=(3, ), name='input_a')
a_2 = Dense(4,
name='dense_1',
kernel_regularizer='l1',
bias_regularizer='l2')(a)
dp = Dropout(0.5, name='dropout')
a_3 = dp(a_2)
model = Model(a, [a_2, a_3])
optimizer = 'rmsprop'
loss = None
model.compile(optimizer, loss, metrics=['mae'])
input_a_np = np.random.random((10, 3))
# test train_on_batch
out = model.train_on_batch(input_a_np, None)
out = model.test_on_batch(input_a_np, None)
# fit
out = model.fit(input_a_np, None)
# evaluate
out = model.evaluate(input_a_np, None)
# No dropout, external loss.
a = Input(shape=(3, ), name='input_a')
a_2 = Dense(4, name='dense_1')(a)
a_3 = Dense(4, name='dense_2')(a)
model = Model(a, [a_2, a_3])
model.add_loss(K.mean(a_3 + a_2))
optimizer = 'rmsprop'
loss = None
model.compile(optimizer, loss, metrics=['mae'])
# test train_on_batch
out = model.train_on_batch(input_a_np, None)
out = model.test_on_batch(input_a_np, None)
# fit
out = model.fit(input_a_np, None)
# evaluate
out = model.evaluate(input_a_np, None)
# Test fit with no external data at all.
if K.backend() == 'tensorflow':
import tensorflow as tf
a = Input(tensor=tf.Variable(input_a_np, dtype=tf.float32))
a_2 = Dense(4, name='dense_1')(a)
a_2 = Dropout(0.5, name='dropout')(a_2)
model = Model(a, a_2)
model.add_loss(K.mean(a_2))
model.compile(optimizer='rmsprop',
loss=None,
metrics=['mean_squared_error'])
# test train_on_batch
out = model.train_on_batch(None, None)
out = model.test_on_batch(None, None)
out = model.predict_on_batch(None)
# test fit
with pytest.raises(ValueError):
out = model.fit(None, None, epochs=1, batch_size=10)
out = model.fit(None, None, epochs=1, steps_per_epoch=1)
# define a generator to produce x=None and y=None
def data_tensors_generator():
while True:
yield (None, None)
generator = data_tensors_generator()
# test fit_generator for framework-native data tensors
out = model.fit_generator(generator, epochs=1, steps_per_epoch=3)
# test evaluate_generator for framework-native data tensors
out = model.evaluate_generator(generator, steps=3)
# test fit with validation data
with pytest.raises(ValueError):
out = model.fit(None,
None,
epochs=1,
steps_per_epoch=None,
validation_steps=2)
out = model.fit(None,
None,
epochs=1,
steps_per_epoch=2,
validation_steps=2)
# test evaluate
with pytest.raises(ValueError):
out = model.evaluate(None, None, batch_size=10)
out = model.evaluate(None, None, steps=3)
# test predict
with pytest.raises(ValueError):
out = model.predict(None, batch_size=10)
out = model.predict(None, steps=3)
assert out.shape == (10 * 3, 4)
# Test multi-output model without external data.
a = Input(tensor=tf.Variable(input_a_np, dtype=tf.float32))
a_1 = Dense(4, name='dense_1')(a)
a_2 = Dropout(0.5, name='dropout')(a_1)
model = Model(a, [a_1, a_2])
model.add_loss(K.mean(a_2))
model.compile(optimizer='rmsprop',
loss=None,
metrics=['mean_squared_error'])
# test train_on_batch
out = model.train_on_batch(None, None)
out = model.test_on_batch(None, None)
out = model.predict_on_batch(None)
# test fit
with pytest.raises(ValueError):
out = model.fit(None, None, epochs=1, batch_size=10)
out = model.fit(None, None, epochs=1, steps_per_epoch=1)
# test fit with validation data
with pytest.raises(ValueError):
out = model.fit(None,
None,
epochs=1,
steps_per_epoch=None,
validation_steps=2)
out = model.fit(None,
None,
epochs=1,
steps_per_epoch=2,
validation_steps=2)
# test evaluate
with pytest.raises(ValueError):
out = model.evaluate(None, None, batch_size=10)
out = model.evaluate(None, None, steps=3)
# test predict
with pytest.raises(ValueError):
out = model.predict(None, batch_size=10)
out = model.predict(None, steps=3)
assert len(out) == 2
assert out[0].shape == (10 * 3, 4)
assert out[1].shape == (10 * 3, 4)
class AE(Model):
"""
Autoencoder. This is a simple autoencoder consisting of an encoder and a decoder.
You can use the class like this:
>>> encoder = ...
>>> decoder = ...
>>> ae = Autoencoder(encoder=encoder, decoder=decoder)
>>> ae.compile(...)
>>> ae.fit(...)
"""
def __init__(self, encoder=None, decoder=None, autoencoder=None):
super(AE, self).__init__()
# For calling this as a super-constructor.
parameters = [encoder, decoder]
if all(v is None for v in parameters):
return
# From loading.
if encoder != None and decoder != None and autoencoder != None:
self.encoder = encoder
self.decoder = decoder
self.autoencoder = autoencoder
return
# Check preconditions.
assert len(encoder.outputs) == 1
assert len(decoder.inputs) == 1
assert encoder.outputs[0].shape[1:] == decoder.inputs[0].shape[
1:], str(encoder.outputs[0].shape) + " " + str(
decoder.inputs[0].shape)
self.latent_dim = encoder.outputs[0].shape[1]
self.encoder = encoder
self.decoder = decoder
# Creating the AE.
inputs = self.encoder.inputs[0]
outputs = self.decoder(self.encoder(inputs))
self.autoencoder = Model(inputs, outputs, name='ae')
def compile(self,
optimizer,
loss=None,
metrics=None,
loss_weights=None,
sample_weight_mode=None,
weighted_metrics=None,
target_tensors=None,
**kwargs):
"""
Compiles the model.
This is the same as compilation in Keras.
"""
assert "reconstruction_loss" not in kwargs, "Not expected to use reconstruction_loss in AE."
self.autoencoder.compile(optimizer, loss, metrics, loss_weights,
sample_weight_mode, weighted_metrics,
**kwargs)
def fit(self,
x=None,
y=None,
batch_size=None,
epochs=1,
verbose=1,
callbacks=None,
validation_split=0.,
validation_data=None,
shuffle=True,
class_weight=None,
sample_weight=None,
initial_epoch=0,
steps_per_epoch=None,
validation_steps=None,
**kwargs):
"""
Trains the autoencoder.
"""
return self.autoencoder.fit(x, y, batch_size, epochs, verbose,
callbacks, validation_split,
validation_data, shuffle, class_weight,
sample_weight, initial_epoch,
steps_per_epoch, validation_steps,
**kwargs)
def fit_generator(self,
generator,
steps_per_epoch=None,
epochs=1,
verbose=1,
callbacks=None,
validation_data=None,
validation_steps=None,
class_weight=None,
max_queue_size=10,
workers=1,
use_multiprocessing=False,
shuffle=True,
initial_epoch=0):
"""
Trains the autoencoder with a generator.
"""
return self.autoencoder.fit_generator(
generator,
steps_per_epoch,
epochs,
verbose=verbose,
callbacks=callbacks,
validation_data=validation_data,
validation_steps=validation_steps,
class_weight=class_weight,
max_queue_size=max_queue_size,
workers=workers,
use_multiprocessing=use_multiprocessing,
shuffle=shuffle,
initial_epoch=initial_epoch)
def evaluate(self,
x=None,
y=None,
batch_size=None,
verbose=1,
sample_weight=None,
steps=None):
"""
Evaluates the autoencoder.
"""
return self.autoencoder.evaluate(x,
y,
batch_size,
verbose,
sample_weight,
steps=None)
def predict(self, x, batch_size=None, verbose=0, steps=None):
"""
Does a prediction. This is the same as :func:`~ngdlm.models.AE.predict_reconstruct_from_samples`
"""
return self.predict_reconstruct_from_samples(x, batch_size, verbose,
steps)
def predict_reconstruct_from_samples(self,
x,
batch_size=None,
verbose=0,
steps=None):
"""
Reconstructs samples.
Samples are firstly mapped to latent space using the encoder.
The resulting latent vectors are then mapped to reconstruction space via the decoder.
"""
return self.autoencoder.predict(x, batch_size, verbose, steps)
def predict_embed_samples_into_latent(self,
x,
batch_size=None,
verbose=0,
steps=None):
"""
Embeds samples into latent space using the encoder.
"""
return self.encoder.predict(x, batch_size, verbose, steps)
def predict_reconstruct_from_latent(self,
x,
batch_size=None,
verbose=0,
steps=None):
"""
Maps latent vectors to reconstruction space using the decoder.
"""
return self.decoder.predict(x, batch_size, verbose, steps)
def summary(self):
"""
Provides a summary.
"""
print("Encoder:")
self.encoder.summary()
print("Decoder:")
self.decoder.summary()
print("Autoencoder:")
self.autoencoder.summary()
def save(self, path):
"""
Saves the autoencoder.
This includes the whole autoencoder plus the encoder and the decoder.
The encoder and decoder use the path plus a respective annotation.
This code
>>> ae.save("myae.h5")
will create the files *myae.h5*, *myae-encoder.h5*, and *myae-decoder.h5*.
"""
self.autoencoder.save(path)
self.encoder.save(append_to_filepath(path, "-encoder"))
self.decoder.save(append_to_filepath(path, "-decoder"))
def test_model_methods():
a = Input(shape=(3,), name='input_a')
b = Input(shape=(3,), name='input_b')
a_2 = Dense(4, name='dense_1')(a)
dp = Dropout(0.5, name='dropout')
b_2 = dp(b)
model = Model([a, b], [a_2, b_2])
optimizer = 'rmsprop'
loss = 'mse'
loss_weights = [1., 0.5]
model.compile(optimizer, loss, metrics=[], loss_weights=loss_weights,
sample_weight_mode=None)
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
# test train_on_batch
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out = model.train_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np])
out = model.train_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np})
# test fit
out = model.fit([input_a_np, input_b_np],
[output_a_np, output_b_np], nb_epoch=1, batch_size=4)
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np], nb_epoch=1, batch_size=4)
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np},
nb_epoch=1, batch_size=4)
# test validation_split
out = model.fit([input_a_np, input_b_np],
[output_a_np, output_b_np],
nb_epoch=1, batch_size=4, validation_split=0.5)
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np],
nb_epoch=1, batch_size=4, validation_split=0.5)
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np},
nb_epoch=1, batch_size=4, validation_split=0.5)
# test validation data
out = model.fit([input_a_np, input_b_np],
[output_a_np, output_b_np],
nb_epoch=1, batch_size=4,
validation_data=([input_a_np, input_b_np], [output_a_np, output_b_np]))
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np],
nb_epoch=1, batch_size=4, validation_split=0.5,
validation_data=({'input_a': input_a_np, 'input_b': input_b_np}, [output_a_np, output_b_np]))
out = model.fit({'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np},
nb_epoch=1, batch_size=4, validation_split=0.5,
validation_data=({'input_a': input_a_np, 'input_b': input_b_np}, {'dense_1': output_a_np, 'dropout': output_b_np}))
# test_on_batch
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out = model.test_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
[output_a_np, output_b_np])
out = model.test_on_batch({'input_a': input_a_np, 'input_b': input_b_np},
{'dense_1': output_a_np, 'dropout': output_b_np})
# predict_on_batch
out = model.predict_on_batch([input_a_np, input_b_np])
out = model.predict_on_batch({'input_a': input_a_np, 'input_b': input_b_np})
# predict, evaluate
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4)
out = model.predict([input_a_np, input_b_np], batch_size=4)
# with sample_weight
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
sample_weight = [None, np.random.random((10,))]
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
# test accuracy metric
model.compile(optimizer, loss, metrics=['acc'],
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 5
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 5
# this should also work
model.compile(optimizer, loss, metrics={'dense_1': 'acc'},
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
# and this as well
model.compile(optimizer, loss, metrics={'dense_1': ['acc']},
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
# test starting from non-zero initial epoch
trained_epochs = []
def on_epoch_begin(epoch, logs):
trained_epochs.append(epoch)
tracker_cb = LambdaCallback(on_epoch_begin=on_epoch_begin)
out = model.fit([input_a_np, input_b_np],
[output_a_np, output_b_np], nb_epoch=5, batch_size=4,
initial_epoch=2, callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
# test starting from non-zero initial epoch for generator too
trained_epochs = []
def gen_data(batch_sz):
while True:
yield ([np.random.random((batch_sz, 3)), np.random.random((batch_sz, 3))],
[np.random.random((batch_sz, 4)), np.random.random((batch_sz, 3))])
out = model.fit_generator(gen_data(4), samples_per_epoch=10, nb_epoch=5,
initial_epoch=2, callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
# test with a custom metric function
mse = lambda y_true, y_pred: K.mean(K.pow(y_true - y_pred, 2))
def mse_powers(y_true, y_pred):
m = mse(y_true, y_pred)
return {
'mse_squared': K.pow(m, 2),
'mse_cubed': K.pow(m, 3)
}
model.compile(optimizer, loss, metrics=[mse, mse_powers],
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out_len = 1 + 2 * 4 # total loss, per layer: loss + 3 metrics
assert len(out) == out_len
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == out_len
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4, nb_epoch=1)
out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np], batch_size=4)
out = model.predict([input_a_np, input_b_np], batch_size=4)
def test_model_methods():
a = Input(shape=(3, ), name='input_a')
b = Input(shape=(3, ), name='input_b')
a_2 = Dense(4, name='dense_1')(a)
dp = Dropout(0.5, name='dropout')
b_2 = dp(b)
model = Model([a, b], [a_2, b_2])
optimizer = 'rmsprop'
loss = 'mse'
loss_weights = [1., 0.5]
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
# training/testing doesn't work before compiling.
with pytest.raises(RuntimeError):
model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
model.compile(optimizer,
loss,
metrics=[],
loss_weights=loss_weights,
sample_weight_mode=None)
# test train_on_batch
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out = model.train_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np])
out = model.train_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
})
# test fit
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
epochs=1,
batch_size=4)
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np],
epochs=1,
batch_size=4)
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
},
epochs=1,
batch_size=4)
# test validation_split
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
epochs=1,
batch_size=4,
validation_split=0.5)
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np],
epochs=1,
batch_size=4,
validation_split=0.5)
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
},
epochs=1,
batch_size=4,
validation_split=0.5)
# test validation data
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
epochs=1,
batch_size=4,
validation_data=([input_a_np,
input_b_np], [output_a_np, output_b_np]))
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np],
epochs=1,
batch_size=4,
validation_split=0.5,
validation_data=({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np]))
out = model.fit({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
},
epochs=1,
batch_size=4,
validation_split=0.5,
validation_data=({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
}))
# test_on_batch
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out = model.test_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
}, [output_a_np, output_b_np])
out = model.test_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
}, {
'dense_1': output_a_np,
'dropout': output_b_np
})
# predict_on_batch
out = model.predict_on_batch([input_a_np, input_b_np])
out = model.predict_on_batch({
'input_a': input_a_np,
'input_b': input_b_np
})
# predict, evaluate
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np],
batch_size=4)
out = model.predict([input_a_np, input_b_np], batch_size=4)
# with sample_weight
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
sample_weight = [None, np.random.random((10, ))]
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
# test accuracy metric
model.compile(optimizer, loss, metrics=['acc'], sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 5
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 5
# this should also work
model.compile(optimizer,
loss,
metrics={'dense_1': 'acc'},
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
# and this as well
model.compile(optimizer,
loss,
metrics={'dense_1': ['acc']},
sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == 4
# test starting from non-zero initial epoch
trained_epochs = []
# define tracer callback
def on_epoch_begin(epoch, logs):
trained_epochs.append(epoch)
tracker_cb = LambdaCallback(on_epoch_begin=on_epoch_begin)
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
epochs=5,
batch_size=4,
initial_epoch=2,
callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
# test starting from non-zero initial epoch for generator too
trained_epochs = []
def gen_data(batch_sz):
while True:
yield ([
np.random.random((batch_sz, 3)),
np.random.random((batch_sz, 3))
], [
np.random.random((batch_sz, 4)),
np.random.random((batch_sz, 3))
])
out = model.fit_generator(gen_data(4),
steps_per_epoch=3,
epochs=5,
initial_epoch=2,
callbacks=[tracker_cb])
assert trained_epochs == [2, 3, 4]
# test with a custom metric function
def mse(y_true, y_pred):
return K.mean(K.pow(y_true - y_pred, 2))
model.compile(optimizer, loss, metrics=[mse], sample_weight_mode=None)
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
out_len = 1 + 2 * (1 + 1) # total loss + 2 outputs * (loss + metric)
assert len(out) == out_len
out = model.test_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np])
assert len(out) == out_len
input_a_np = np.random.random((10, 3))
input_b_np = np.random.random((10, 3))
output_a_np = np.random.random((10, 4))
output_b_np = np.random.random((10, 3))
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
batch_size=4,
epochs=1)
out = model.evaluate([input_a_np, input_b_np], [output_a_np, output_b_np],
batch_size=4)
out = model.predict([input_a_np, input_b_np], batch_size=4)
# x is not a list of numpy arrays.
with pytest.raises(ValueError):
out = model.predict([None])
# x does not match _feed_input_names.
with pytest.raises(ValueError):
out = model.predict([input_a_np, None, input_b_np])
with pytest.raises(ValueError):
out = model.predict([None, input_a_np, input_b_np])
# all input/output/weight arrays should have the same number of samples.
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np[:2]],
[output_a_np, output_b_np],
sample_weight=sample_weight)
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np[:2]],
sample_weight=sample_weight)
with pytest.raises(ValueError):
out = model.train_on_batch(
[input_a_np, input_b_np], [output_a_np, output_b_np],
sample_weight=[sample_weight[1], sample_weight[1][:2]])
# `sample_weight` is neither a dict nor a list.
with pytest.raises(TypeError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=tuple(sample_weight))
# `validation_data` is neither a tuple nor a triple.
with pytest.raises(ValueError):
out = model.fit([input_a_np, input_b_np], [output_a_np, output_b_np],
epochs=1,
batch_size=4,
validation_data=([input_a_np, input_b_np], ))
# `loss` does not match outputs.
with pytest.raises(ValueError):
model.compile(optimizer, loss=['mse', 'mae', 'mape'])
# `loss_weights` does not match output_names.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', loss_weights={'lstm': 0.5})
# `loss_weights` does not match outputs.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', loss_weights=[0.5])
# `loss_weights` is invalid type.
with pytest.raises(TypeError):
model.compile(optimizer, loss='mse', loss_weights=(0.5, 0.5))
# `sample_weight_mode` does not match output_names.
with pytest.raises(ValueError):
model.compile(optimizer,
loss='mse',
sample_weight_mode={'lstm': 'temporal'})
# `sample_weight_mode` does not match output_names.
with pytest.raises(ValueError):
model.compile(optimizer, loss='mse', sample_weight_mode=['temporal'])
# `sample_weight_mode` matches output_names partially.
with pytest.raises(ValueError):
model.compile(optimizer,
loss='mse',
sample_weight_mode={'dense_1': 'temporal'})
# `loss` does not exist.
with pytest.raises(RuntimeError):
model.compile(optimizer, loss=[])
model.compile(optimizer, loss=['mse', 'mae'])
model.compile(optimizer,
loss='mse',
loss_weights={
'dense_1': 0.2,
'dropout': 0.8
})
model.compile(optimizer, loss='mse', loss_weights=[0.2, 0.8])
# the rank of weight arrays should be 1.
with pytest.raises(ValueError):
out = model.train_on_batch(
[input_a_np, input_b_np], [output_a_np, output_b_np],
sample_weight=[None, np.random.random((10, 20, 30))])
model.compile(optimizer,
loss='mse',
sample_weight_mode={
'dense_1': None,
'dropout': 'temporal'
})
model.compile(optimizer, loss='mse', sample_weight_mode=[None, 'temporal'])
# the rank of output arrays should be at least 3D.
with pytest.raises(ValueError):
out = model.train_on_batch([input_a_np, input_b_np],
[output_a_np, output_b_np],
sample_weight=sample_weight)
