# 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.