Zeros

tflearn.initializations.zeros (shape=None, dtype=tf.float32, seed=None)

Initialize a tensor with all elements set to zero.

Arguments

shape: List of int. A shape to initialize a Tensor (optional).
dtype: The tensor data type.

Returns

The Initializer, or an initialized Tensor if a shape is specified.

Uniform

tflearn.initializations.uniform (shape=None, minval=0, maxval=None, dtype=tf.float32, seed=None)

Initialization with random values from a uniform distribution.

The generated values follow a uniform distribution in the range [minval, maxval). The lower bound minval is included in the range, while the upper bound maxval is excluded.

For floats, the default range is [0, 1). For ints, at least maxval must be specified explicitly.

In the integer case, the random integers are slightly biased unless maxval - minval is an exact power of two. The bias is small for values of maxval - minval significantly smaller than the range of the output (either 2**32 or 2**64).

Arguments

shape: List of int. A shape to initialize a Tensor (optional).
dtype: The tensor data type. Only float are supported.
seed: int. Used to create a random seed for the distribution.

Returns

The Initializer, or an initialized Tensor if shape is specified.

Uniform Scaling

tflearn.initializations.uniform_scaling (shape=None, factor=1.0, dtype=tf.float32, seed=None)

Initialization with random values from uniform distribution without scaling variance.

When initializing a deep network, it is in principle advantageous to keep the scale of the input variance constant, so it does not explode or diminish by reaching the final layer. If the input is x and the operation x * W, and we want to initialize W uniformly at random, we need to pick W from

[-sqrt(3) / sqrt(dim), sqrt(3) / sqrt(dim)]

to keep the scale intact, where dim = W.shape[0] (the size of the input). A similar calculation for convolutional networks gives an analogous result with dim equal to the product of the first 3 dimensions. When nonlinearities are present, we need to multiply this by a constant factor. See Sussillo et al., 2014 (pdf) for deeper motivation, experiments and the calculation of constants. In section 2.3 there, the constants were numerically computed: for a linear layer it's 1.0, relu: ~1.43, tanh: ~1.15.

Arguments

shape: List of int. A shape to initialize a Tensor (optional).
factor: float. A multiplicative factor by which the values will be scaled.
dtype: The tensor data type. Only float are supported.
seed: int. Used to create a random seed for the distribution.

Returns

The Initializer, or an initialized Tensor if shape is specified.

Normal

tflearn.initializations.normal (shape=None, mean=0.0, stddev=0.02, dtype=tf.float32, seed=None)

Initialization with random values from a normal distribution.

Arguments

shape: List of int. A shape to initialize a Tensor (optional).
mean: Same as dtype. The mean of the truncated normal distribution.
stddev: Same as dtype. The standard deviation of the truncated normal distribution.
dtype: The tensor data type.
seed: int. Used to create a random seed for the distribution.

Returns

The Initializer, or an initialized Tensor if shape is specified.

Truncated Normal

tflearn.initializations.truncated_normal (shape=None, mean=0.0, stddev=0.02, dtype=tf.float32, seed=None)

Initialization with random values from a normal truncated distribution.

The generated values follow a normal distribution with specified mean and standard deviation, except that values whose magnitude is more than 2 standard deviations from the mean are dropped and re-picked.

Arguments

shape: List of int. A shape to initialize a Tensor (optional).
mean: Same as dtype. The mean of the truncated normal distribution.
stddev: Same as dtype. The standard deviation of the truncated normal distribution.
dtype: The tensor data type.
seed: int. Used to create a random seed for the distribution.

Returns

The Initializer, or an initialized Tensor if shape is specified.

Xavier

tflearn.initializations.xavier (uniform=True, seed=None, dtype=tf.float32)

Returns an initializer performing "Xavier" initialization for weights.

This initializer is designed to keep the scale of the gradients roughly the same in all layers. In uniform distribution this ends up being the range: x = sqrt(6. / (in + out)); [-x, x] and for normal distribution a standard deviation of sqrt(3. / (in + out)) is used.

Arguments

uniform: Whether to use uniform or normal distributed random initialization.
seed: A Python integer. Used to create random seeds. See set_random_seed for behavior.
dtype: The data type. Only floating point types are supported.

Returns

An initializer for a weight matrix.

References

Understanding the difficulty of training deep feedforward neural networks. International conference on artificial intelligence and statistics. Xavier Glorot and Yoshua Bengio (2010).

Links

http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf

Variance Scaling

tflearn.initializations.variance_scaling (factor=2.0, mode='FAN_IN', uniform=False, seed=None, dtype=tf.float32)

Returns an initializer that generates tensors without scaling variance.

When initializing a deep network, it is in principle advantageous to keep the scale of the input variance constant, so it does not explode or diminish by reaching the final layer. This initializer use the following formula:

if mode='FAN_IN': # Count only number of input connections.
  n = fan_in
elif mode='FAN_OUT': # Count only number of output connections.
  n = fan_out
elif mode='FAN_AVG': # Average number of inputs and output connections.
  n = (fan_in + fan_out)/2.0

  truncated_normal(shape, 0.0, stddev=sqrt(factor / n))

To get http://arxiv.org/pdf/1502.01852v1.pdf use (Default): - factor=2.0 mode='FAN_IN' uniform=False

To get http://arxiv.org/abs/1408.5093 use: - factor=1.0 mode='FAN_IN' uniform=True

To get http://jmlr.org/proceedings/papers/v9/glorot10a/glorot10a.pdf use: - factor=1.0 mode='FAN_AVG' uniform=True.

To get xavier_initializer use either: - factor=1.0 mode='FAN_AVG' uniform=True. - factor=1.0 mode='FAN_AVG' uniform=False.

Arguments

factor: Float. A multiplicative factor.
mode: String. 'FAN_IN', 'FAN_OUT', 'FAN_AVG'.
uniform: Whether to use uniform or normal distributed random initialization.
seed: A Python integer. Used to create random seeds. See set_random_seed for behavior.
dtype: The data type. Only floating point types are supported.

Returns

An initializer that generates tensors with unit variance.