def test_saving_overwrite_option_gcs():
model = Sequential()
model.add(Dense(2, input_shape=(3, )))
org_weights = model.get_weights()
new_weights = [np.random.random(w.shape) for w in org_weights]
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(
filename='test_saving_overwrite_option_gcs.h5')
# we should not use same filename in several tests to allow for parallel
# execution
save_model(model, gcs_filepath)
model.set_weights(new_weights)
with patch('keras.engine.saving.ask_to_proceed_with_overwrite') as ask:
ask.return_value = False
save_model(model, gcs_filepath, overwrite=False)
ask.assert_called_once()
new_model = load_model(gcs_filepath)
for w, org_w in zip(new_model.get_weights(), org_weights):
assert_allclose(w, org_w)
ask.return_value = True
save_model(model, gcs_filepath, overwrite=False)
assert ask.call_count == 2
new_model = load_model(gcs_filepath)
for w, new_w in zip(new_model.get_weights(), new_weights):
assert_allclose(w, new_w)
file_io_proxy.delete_file(gcs_filepath) # cleanup
def test_sequential_model_saving_2():
# test with custom optimizer, loss
custom_opt = optimizers.rmsprop
custom_loss = losses.mse
model = Sequential()
model.add(Dense(2, input_shape=(3,)))
model.add(Dense(3))
model.compile(loss=custom_loss, optimizer=custom_opt(), metrics=['acc'])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
load_kwargs = {'custom_objects': {'custom_opt': custom_opt,
'custom_loss': custom_loss}}
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
new_model_disk = load_model(fname, **load_kwargs)
os.remove(fname)
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(filename=fname)
save_model(model, gcs_filepath)
file_io_proxy.assert_exists(gcs_filepath)
new_model_gcs = load_model(gcs_filepath, **load_kwargs)
file_io_proxy.delete_file(gcs_filepath) # cleanup
for new_model in [new_model_disk, new_model_gcs]:
new_out = new_model.predict(x)
assert_allclose(out, new_out, atol=1e-05)
def test_sequential_model_saving_2():
# test with custom optimizer, loss
custom_opt = optimizers.rmsprop
custom_loss = losses.mse
model = Sequential()
model.add(Dense(2, input_shape=(3, )))
model.add(Dense(3))
model.compile(loss=custom_loss, optimizer=custom_opt(), metrics=['acc'])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
load_kwargs = {
'custom_objects': {
'custom_opt': custom_opt,
'custom_loss': custom_loss
}
}
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
new_model_disk = load_model(fname, **load_kwargs)
os.remove(fname)
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(filename=fname)
save_model(model, gcs_filepath)
file_io_proxy.assert_exists(gcs_filepath)
new_model_gcs = load_model(gcs_filepath, **load_kwargs)
file_io_proxy.delete_file(gcs_filepath) # cleanup
for new_model in [new_model_disk, new_model_gcs]:
new_out = new_model.predict(x)
assert_allclose(out, new_out, atol=1e-05)
def test_functional_model_saving():
inputs = Input(shape=(3, ))
x = Dense(2)(inputs)
outputs = Dense(3)(x)
model = Model(inputs, outputs)
model.compile(loss=losses.MSE,
optimizer=optimizers.Adam(),
metrics=[metrics.categorical_accuracy])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
new_model_disk = load_model(fname)
os.remove(fname)
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(filename=fname)
save_model(model, gcs_filepath)
file_io_proxy.assert_exists(gcs_filepath)
new_model_gcs = load_model(gcs_filepath)
file_io_proxy.delete_file(gcs_filepath) # cleanup
for new_model in [new_model_disk, new_model_gcs]:
new_out = new_model.predict(x)
assert_allclose(out, new_out, atol=1e-05)
def test_saving_overwrite_option_gcs():
model = Sequential()
model.add(Dense(2, input_shape=(3,)))
org_weights = model.get_weights()
new_weights = [np.random.random(w.shape) for w in org_weights]
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(
filename='test_saving_overwrite_option_gcs.h5')
# we should not use same filename in several tests to allow for parallel
# execution
save_model(model, gcs_filepath)
model.set_weights(new_weights)
with patch('keras.engine.saving.ask_to_proceed_with_overwrite') as ask:
ask.return_value = False
save_model(model, gcs_filepath, overwrite=False)
ask.assert_called_once()
new_model = load_model(gcs_filepath)
for w, org_w in zip(new_model.get_weights(), org_weights):
assert_allclose(w, org_w)
ask.return_value = True
save_model(model, gcs_filepath, overwrite=False)
assert ask.call_count == 2
new_model = load_model(gcs_filepath)
for w, new_w in zip(new_model.get_weights(), new_weights):
assert_allclose(w, new_w)
file_io_proxy.delete_file(gcs_filepath) # cleanup
def test_functional_model_saving():
inputs = Input(shape=(3,))
x = Dense(2)(inputs)
outputs = Dense(3)(x)
model = Model(inputs, outputs)
model.compile(loss=losses.MSE,
optimizer=optimizers.Adam(),
metrics=[metrics.categorical_accuracy])
x = np.random.random((1, 3))
y = np.random.random((1, 3))
model.train_on_batch(x, y)
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
new_model_disk = load_model(fname)
os.remove(fname)
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(filename=fname)
save_model(model, gcs_filepath)
file_io_proxy.assert_exists(gcs_filepath)
new_model_gcs = load_model(gcs_filepath)
file_io_proxy.delete_file(gcs_filepath) # cleanup
for new_model in [new_model_disk, new_model_gcs]:
new_out = new_model.predict(x)
assert_allclose(out, new_out, atol=1e-05)
def test_functional_model_saving():
model, x = _get_sample_model_and_input()
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
new_model_disk = load_model(fname)
os.remove(fname)
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(filename=fname)
save_model(model, gcs_filepath)
file_io_proxy.assert_exists(gcs_filepath)
new_model_gcs = load_model(gcs_filepath)
file_io_proxy.delete_file(gcs_filepath) # cleanup
for new_model in [new_model_disk, new_model_gcs]:
new_out = new_model.predict(x)
assert_allclose(out, new_out, atol=1e-05)
def test_functional_model_saving():
model, x = _get_sample_model_and_input()
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
new_model_disk = load_model(fname)
os.remove(fname)
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(filename=fname)
save_model(model, gcs_filepath)
file_io_proxy.assert_exists(gcs_filepath)
new_model_gcs = load_model(gcs_filepath)
file_io_proxy.delete_file(gcs_filepath) # cleanup
for new_model in [new_model_disk, new_model_gcs]:
new_out = new_model.predict(x)
assert_allclose(out, new_out, atol=1e-05)
def test_save_load_weights_gcs():
model = Sequential()
model.add(Dense(2, input_shape=(3, )))
org_weights = model.get_weights()
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(
filename='test_save_load_weights_gcs.h5')
# we should not use same filename in several tests to allow for parallel
# execution
model.save_weights(gcs_filepath)
model.set_weights([np.random.random(w.shape) for w in org_weights])
for w, org_w in zip(model.get_weights(), org_weights):
assert not (w == org_w).all()
model.load_weights(gcs_filepath)
for w, org_w in zip(model.get_weights(), org_weights):
assert_allclose(w, org_w)
file_io_proxy.delete_file(gcs_filepath) # cleanup
def test_save_load_weights_gcs():
model = Sequential()
model.add(Dense(2, input_shape=(3,)))
org_weights = model.get_weights()
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(
filename='test_save_load_weights_gcs.h5')
# we should not use same filename in several tests to allow for parallel
# execution
model.save_weights(gcs_filepath)
model.set_weights([np.random.random(w.shape) for w in org_weights])
for w, org_w in zip(model.get_weights(), org_weights):
assert not (w == org_w).all()
model.load_weights(gcs_filepath)
for w, org_w in zip(model.get_weights(), org_weights):
assert_allclose(w, org_w)
file_io_proxy.delete_file(gcs_filepath) # cleanup
def test_sequential_model_saving():
model = Sequential()
model.add(Dense(2, input_shape=(3, )))
model.add(RepeatVector(3))
model.add(TimeDistributed(Dense(3)))
model.compile(loss=losses.MSE,
optimizer=optimizers.RMSprop(lr=0.0001),
metrics=[metrics.categorical_accuracy],
sample_weight_mode='temporal')
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
new_model_disk = load_model(fname)
os.remove(fname)
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(filename=fname)
save_model(model, gcs_filepath)
file_io_proxy.assert_exists(gcs_filepath)
new_model_gcs = load_model(gcs_filepath)
file_io_proxy.delete_file(gcs_filepath) # cleanup
x2 = np.random.random((1, 3))
y2 = np.random.random((1, 3, 3))
model.train_on_batch(x2, y2)
out_2 = model.predict(x2)
for new_model in [new_model_disk, new_model_gcs]:
new_out = new_model.predict(x)
assert_allclose(out, new_out, atol=1e-05)
# test that new updates are the same with both models
new_model.train_on_batch(x2, y2)
new_out_2 = new_model.predict(x2)
assert_allclose(out_2, new_out_2, atol=1e-05)
def test_sequential_model_saving():
model = Sequential()
model.add(Dense(2, input_shape=(3,)))
model.add(RepeatVector(3))
model.add(TimeDistributed(Dense(3)))
model.compile(loss=losses.MSE,
optimizer=optimizers.RMSprop(lr=0.0001),
metrics=[metrics.categorical_accuracy],
sample_weight_mode='temporal')
x = np.random.random((1, 3))
y = np.random.random((1, 3, 3))
model.train_on_batch(x, y)
out = model.predict(x)
_, fname = tempfile.mkstemp('.h5')
save_model(model, fname)
new_model_disk = load_model(fname)
os.remove(fname)
with tf_file_io_proxy('keras.engine.saving.tf_file_io') as file_io_proxy:
gcs_filepath = file_io_proxy.get_filepath(filename=fname)
save_model(model, gcs_filepath)
file_io_proxy.assert_exists(gcs_filepath)
new_model_gcs = load_model(gcs_filepath)
file_io_proxy.delete_file(gcs_filepath) # cleanup
x2 = np.random.random((1, 3))
y2 = np.random.random((1, 3, 3))
model.train_on_batch(x2, y2)
out_2 = model.predict(x2)
for new_model in [new_model_disk, new_model_gcs]:
new_out = new_model.predict(x)
assert_allclose(out, new_out, atol=1e-05)
# test that new updates are the same with both models
new_model.train_on_batch(x2, y2)
new_out_2 = new_model.predict(x2)
assert_allclose(out_2, new_out_2, atol=1e-05)
