def dropped_inputs(inputs=inputs, rate=self.rate, seed=self.seed):
alpha_p = -alpha * scale
kept_idx = K.greater_equal(K.random_uniform(noise_shape, seed=seed),
rate)
kept_idx = K.cast(kept_idx, K.floatx())
a = ((1 - rate) * (1 + rate * alpha_p ** 2)) ** -0.5
b = -a * alpha_p * rate
x = inputs * kept_idx + alpha_p * (1 - kept_idx)
return a * x + b
def dropped_inputs(inputs=inputs, rate=self.rate, seed=self.seed):
alpha_p = -alpha * scale
kept_idx = K.greater_equal(K.random_uniform(noise_shape, seed=seed),
rate)
kept_idx = K.cast(kept_idx, K.floatx())
a = ((1 - rate) * (1 + rate * alpha_p ** 2)) ** -0.5
b = -a * alpha_p * rate
x = inputs * kept_idx + alpha_p * (1 - kept_idx)
return a * x + b
def dropped_inputs(inputs=inputs, rate=self.rate, seed=self.seed): # pylint: disable=missing-docstring
alpha = 1.6732632423543772848170429916717
scale = 1.0507009873554804934193349852946
alpha_p = -alpha * scale
kept_idx = K.greater_equal(
K.random_uniform(noise_shape, seed=seed), rate)
kept_idx = K.cast(kept_idx, K.floatx())
# Get affine transformation params
a = ((1 - rate) * (1 + rate * alpha_p**2))**-0.5
b = -a * alpha_p * rate
# Apply mask
x = inputs * kept_idx + alpha_p * (1 - kept_idx)
# Do affine transformation
return a * x + b
def dropped_inputs(inputs=inputs, rate=self.rate, seed=self.seed): # pylint: disable=missing-docstring
alpha = 1.6732632423543772848170429916717
scale = 1.0507009873554804934193349852946
alpha_p = -alpha * scale
kept_idx = K.greater_equal(
K.random_uniform(noise_shape, seed=seed), rate)
kept_idx = K.cast(kept_idx, K.floatx())
# Get affine transformation params
a = ((1 - rate) * (1 + rate * alpha_p**2))**-0.5
b = -a * alpha_p * rate
# Apply mask
x = inputs * kept_idx + alpha_p * (1 - kept_idx)
# Do affine transformation
return a * x + b
def __call__(self, w):
w *= K.cast(K.greater_equal(w, 0.), K.floatx())
return w
def __call__(self, w): return w * K.cast(K.greater_equal(w, 0.), K.floatx())