def test_training(self):
input_layer = keras.layers.Input(shape=(100, 3))
drop_block_layer = DropBlock1D(block_size=3, keep_prob=0.7)(input_layer)
model = keras.models.Model(inputs=input_layer, outputs=drop_block_layer)
model.compile(optimizer='adam', loss='mse', metrics={})
model_path = os.path.join(tempfile.gettempdir(), 'keras_drop_block_%f.h5' % random.random())
model.save(model_path)
model = keras.models.load_model(
model_path,
custom_objects={'DropBlock1D': DropBlock1D},
)
model.summary()
inputs = np.ones((1, 100, 3))
outputs = model.predict(inputs)
self.assertTrue(np.allclose(inputs, outputs))
input_layer = keras.layers.Input(shape=(3, 100))
drop_block_layer = DropBlock1D(block_size=3, keep_prob=0.7, data_format='channels_first')(input_layer)
model = keras.models.Model(inputs=input_layer, outputs=drop_block_layer)
model.compile(optimizer='adam', loss='mse', metrics={})
model_path = os.path.join(tempfile.gettempdir(), 'keras_drop_block_%f.h5' % random.random())
model.save(model_path)
model = keras.models.load_model(
model_path,
custom_objects={'DropBlock1D': DropBlock1D},
)
model.summary()
inputs = np.ones((1, 3, 100))
outputs = model.predict(inputs)
self.assertTrue(np.allclose(inputs, outputs))
def test_mask_shape(self):
input_layer = keras.layers.Input(shape=(100, 3))
drop_block_layer = DropBlock1D(block_size=3,
keep_prob=0.7)(input_layer,
training=True)
model = keras.models.Model(inputs=input_layer,
outputs=drop_block_layer)
model.compile(optimizer='adam', loss='mse', metrics={})
model_path = os.path.join(tempfile.gettempdir(),
'keras_drop_block_%f.h5' % random.random())
model.save(model_path)
model = keras.models.load_model(
model_path,
custom_objects={'DropBlock1D': DropBlock1D},
)
model.summary()
inputs = np.ones((1, 100, 3))
outputs = model.predict(inputs)
for i in range(3):
print((outputs[0, :, i] > 0.0).astype(dtype='int32'))
inputs = np.ones((1000, 100, 3))
outputs = model.predict(inputs)
keep_prob = 1.0 * np.sum(outputs > 0.0) / np.prod(np.shape(outputs))
print(keep_prob)
self.assertTrue(0.65 < keep_prob < 0.8, keep_prob)
input_layer = keras.layers.Input(shape=(3, 100))
drop_block_layer = DropBlock1D(block_size=3,
keep_prob=0.7,
data_format='channels_first')(
input_layer, training=True)
model = keras.models.Model(inputs=input_layer,
outputs=drop_block_layer)
model.compile(optimizer='adam', loss='mse', metrics={})
model_path = os.path.join(tempfile.gettempdir(),
'keras_drop_block_%f.h5' % random.random())
model.save(model_path)
model = keras.models.load_model(
model_path,
custom_objects={'DropBlock1D': DropBlock1D},
)
model.summary()
inputs = np.ones((1, 3, 100))
outputs = model.predict(inputs)
for i in range(3):
print((outputs[0, i, :] > 0.0).astype(dtype='int32'))
inputs = np.ones((1000, 3, 100))
outputs = model.predict(inputs)
keep_prob = 1.0 * np.sum(outputs > 0.0) / np.prod(np.shape(outputs))
print(keep_prob)
self.assertTrue(0.65 < keep_prob < 0.8, keep_prob)
def test_sync_channels(self):
input_layer = keras.layers.Input(shape=(100, 3))
drop_block_layer = keras.layers.Lambda(
lambda x: DropBlock1D(block_size=3, keep_prob=0.7, sync_channels=True)(x, training=True),
)(input_layer)
model = keras.models.Model(inputs=input_layer, outputs=drop_block_layer)
model.compile(optimizer='adam', loss='mse', metrics={})
model_path = os.path.join(tempfile.gettempdir(), 'keras_drop_block_%f.h5' % random.random())
model.save(model_path)
model = keras.models.load_model(
model_path,
custom_objects={'DropBlock1D': DropBlock1D},
)
model.summary()
inputs = np.ones((1, 100, 3))
outputs = model.predict(inputs)
for i in range(1, 3):
self.assertTrue(np.allclose(outputs[0, :, 0], outputs[0, :, i]))
inputs = np.ones((1000, 100, 3))
outputs = model.predict(inputs)
keep_prob = 1.0 * np.sum(outputs > 0.0) / np.prod(np.shape(outputs))
print(keep_prob)
self.assertTrue(0.65 < keep_prob < 0.8, keep_prob)