tf.random_uniform()

tf.random_uniform(shape, minval=0, maxval=None, dtype=tf.float32, seed=None, name=None) Outputs random values from a uniform distribution

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tf.zeros_like()

tf.zeros_like(tensor, dtype=None, name=None, optimize=True) Creates a tensor with all elements set to zero.

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tf.ones_like()

tf.ones_like(tensor, dtype=None, name=None, optimize=True) Creates a tensor with all elements set to 1. Given

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tf.set_random_seed()

tf.set_random_seed(seed) Sets the graph-level random seed. Operations that rely on a random seed

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tf.multinomial()

tf.multinomial(logits, num_samples, seed=None, name=None) Draws samples from a multinomial distribution.

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tf.truncated_normal()

tf.truncated_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None) Outputs random values from a truncated

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tf.random_normal()

tf.random_normal(shape, mean=0.0, stddev=1.0, dtype=tf.float32, seed=None, name=None) Outputs random values from a normal distribution

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tf.random_gamma()

tf.random_gamma(shape, alpha, beta=None, dtype=tf.float32, seed=None, name=None) Draws shape samples from each of

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tf.random_crop()

tf.random_crop(value, size, seed=None, name=None) Randomly crops a tensor to a given size. Slices

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tf.constant()

tf.constant(value, dtype=None, shape=None, name='Const') Creates a constant tensor. The resulting

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