pixel_norm op
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@@ -204,7 +204,7 @@ def random_binomial(shape, p=0.0, dtype=None, seed=None):
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seed = np.random.randint(10e6)
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seed = np.random.randint(10e6)
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return array_ops.where(
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return array_ops.where(
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random_ops.random_uniform(shape, dtype=tf.float16, seed=seed) < p,
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random_ops.random_uniform(shape, dtype=tf.float16, seed=seed) < p,
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array_ops.ones(shape, dtype=dtype), array_ops.zeros(shape, dtype=dtype))
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array_ops.ones(shape, dtype=dtype), array_ops.zeros(shape, dtype=dtype))
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nn.random_binomial = random_binomial
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nn.random_binomial = random_binomial
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def gaussian_blur(input, radius=2.0):
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def gaussian_blur(input, radius=2.0):
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@@ -391,6 +391,11 @@ def total_variation_mse(images):
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return tot_var
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return tot_var
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nn.total_variation_mse = total_variation_mse
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nn.total_variation_mse = total_variation_mse
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def pixel_norm(x, axes):
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return x * tf.rsqrt(tf.reduce_mean(tf.square(x), axis=axes, keepdims=True) + 1e-06)
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nn.pixel_norm = pixel_norm
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"""
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"""
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def tf_suppress_lower_mean(t, eps=0.00001):
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def tf_suppress_lower_mean(t, eps=0.00001):
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if t.shape.ndims != 1:
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if t.shape.ndims != 1:
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