Dense
keras.layers.Dense(units, activation=None, use_bias=True, kernel_initializer='glorot_uniform', bias_initializer='zeros', kernel_regularizer=None, bias_regularizer=None, activity_regularizer=None, kernel_constraint=None, bias_constraint=None)
Just your regular densely-connected NN layer.
Dense
implements the operation:
output = activation(dot(input, kernel) + bias)
where activation
is the element-wise activation function
passed as the activation
argument, kernel
is a weights matrix
created by the layer, and bias
is a bias vector created by the layer
(only applicable if use_bias
is True
).
- Note: if the input to the layer has a rank greater than 2, then
it is flattened prior to the initial dot product with
kernel
.
Example
# as first layer in a sequential model:
model = Sequential()
model.add(Dense(32, input_shape=(16,)))
# now the model will take as input arrays of shape (*, 16)
# and output arrays of shape (*, 32)
# after the first layer, you don't need to specify
# the size of the input anymore:
model.add(Dense(32))
Arguments
- units: Positive integer, dimensionality of the output space.
- activation: Activation function to use
(see activations).
If you don't specify anything, no activation is applied
(ie. "linear" activation:
a(x) = x
). - use_bias: Boolean, whether the layer uses a bias vector.
- kernel_initializer: Initializer for the
kernel
weights matrix (see initializers). - bias_initializer: Initializer for the bias vector (see initializers).
- kernel_regularizer: Regularizer function applied to
the
kernel
weights matrix (see regularizer). - bias_regularizer: Regularizer function applied to the bias vector (see regularizer).
- activity_regularizer: Regularizer function applied to the output of the layer (its "activation"). (see regularizer).
- kernel_constraint: Constraint function applied to
the
kernel
weights matrix (see constraints). - bias_constraint: Constraint function applied to the bias vector (see constraints).
Input shape
nD tensor with shape: (batch_size, ..., input_dim)
.
The most common situation would be
a 2D input with shape (batch_size, input_dim)
.
Output shape
nD tensor with shape: (batch_size, ..., units)
.
For instance, for a 2D input with shape (batch_size, input_dim)
,
the output would have shape (batch_size, units)
.
Activation
keras.layers.Activation(activation)
Applies an activation function to an output.
Arguments
- activation: name of activation function to use (see: activations), or alternatively, a Theano or TensorFlow operation.
Input shape
Arbitrary. Use the keyword argument input_shape
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape
Same shape as input.
Dropout
keras.layers.Dropout(rate, noise_shape=None, seed=None)
Applies Dropout to the input.
Dropout consists in randomly setting
a fraction rate
of input units to 0 at each update during training time,
which helps prevent overfitting.
Arguments
- rate: float between 0 and 1. Fraction of the input units to drop.
- noise_shape: 1D integer tensor representing the shape of the
binary dropout mask that will be multiplied with the input.
For instance, if your inputs have shape
(batch_size, timesteps, features)
and you want the dropout mask to be the same for all timesteps, you can usenoise_shape=(batch_size, 1, features)
. - seed: A Python integer to use as random seed.
References
Flatten
keras.layers.Flatten()
Flattens the input. Does not affect the batch size.
Example
model = Sequential()
model.add(Conv2D(64, 3, 3,
border_mode='same',
input_shape=(3, 32, 32)))
# now: model.output_shape == (None, 64, 32, 32)
model.add(Flatten())
# now: model.output_shape == (None, 65536)
Input
keras.engine.topology.Input()
Input()
is used to instantiate a Keras tensor.
A Keras tensor is a tensor object from the underlying backend (Theano, TensorFlow or CNTK), which we augment with certain attributes that allow us to build a Keras model just by knowing the inputs and outputs of the model.
For instance, if a, b and c are Keras tensors,
it becomes possible to do:
model = Model(input=[a, b], output=c)
The added Keras attributes are:
- _keras_shape
: Integer shape tuple propagated
via Keras-side shape inference.
- _keras_history
: Last layer applied to the tensor.
the entire layer graph is retrievable from that layer,
recursively.
Arguments
- shape: A shape tuple (integer), not including the batch size.
For instance,
shape=(32,)
indicates that the expected input will be batches of 32-dimensional vectors. - batch_shape: A shape tuple (integer), including the batch size.
For instance,
batch_shape=(10, 32)
indicates that the expected input will be batches of 10 32-dimensional vectors.batch_shape=(None, 32)
indicates batches of an arbitrary number of 32-dimensional vectors. - name: An optional name string for the layer. Should be unique in a model (do not reuse the same name twice). It will be autogenerated if it isn't provided.
- dtype: The data type expected by the input, as a string
(
float32
,float64
,int32
...) - sparse: A boolean specifying whether the placeholder to be created is sparse.
- tensor: Optional existing tensor to wrap into the
Input
layer. If set, the layer will not create a placeholder tensor.
Returns
A tensor.
Example
# this is a logistic regression in Keras
x = Input(shape=(32,))
y = Dense(16, activation='softmax')(x)
model = Model(x, y)
Reshape
keras.layers.Reshape(target_shape)
Reshapes an output to a certain shape.
Arguments
- target_shape: target shape. Tuple of integers. Does not include the batch axis.
Input shape
Arbitrary, although all dimensions in the input shaped must be fixed.
Use the keyword argument input_shape
(tuple of integers, does not include the batch axis)
when using this layer as the first layer in a model.
Output shape
(batch_size,) + target_shape
Example
# as first layer in a Sequential model
model = Sequential()
model.add(Reshape((3, 4), input_shape=(12,)))
# now: model.output_shape == (None, 3, 4)
# note: `None` is the batch dimension
# as intermediate layer in a Sequential model
model.add(Reshape((6, 2)))
# now: model.output_shape == (None, 6, 2)
# also supports shape inference using `-1` as dimension
model.add(Reshape((-1, 2, 2)))
# now: model.output_shape == (None, 3, 2, 2)
Permute
keras.layers.Permute(dims)
Permutes the dimensions of the input according to a given pattern.
Useful for e.g. connecting RNNs and convnets together.
Example
model = Sequential()
model.add(Permute((2, 1), input_shape=(10, 64)))
# now: model.output_shape == (None, 64, 10)
# note: `None` is the batch dimension
Arguments
- dims: Tuple of integers. Permutation pattern, does not include the
samples dimension. Indexing starts at 1.
For instance,
(2, 1)
permutes the first and second dimension of the input.
Input shape
Arbitrary. Use the keyword argument input_shape
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape
Same as the input shape, but with the dimensions re-ordered according to the specified pattern.
RepeatVector
keras.layers.RepeatVector(n)
Repeats the input n times.
Example
model = Sequential()
model.add(Dense(32, input_dim=32))
# now: model.output_shape == (None, 32)
# note: `None` is the batch dimension
model.add(RepeatVector(3))
# now: model.output_shape == (None, 3, 32)
Arguments
- n: integer, repetition factor.
Input shape
2D tensor of shape (num_samples, features)
.
Output shape
3D tensor of shape (num_samples, n, features)
.
Lambda
keras.layers.Lambda(function, output_shape=None, mask=None, arguments=None)
Wraps arbitrary expression as a Layer
object.
Examples
# add a x -> x^2 layer
model.add(Lambda(lambda x: x ** 2))
# add a layer that returns the concatenation
# of the positive part of the input and
# the opposite of the negative part
def antirectifier(x):
x -= K.mean(x, axis=1, keepdims=True)
x = K.l2_normalize(x, axis=1)
pos = K.relu(x)
neg = K.relu(-x)
return K.concatenate([pos, neg], axis=1)
def antirectifier_output_shape(input_shape):
shape = list(input_shape)
assert len(shape) == 2 # only valid for 2D tensors
shape[-1] *= 2
return tuple(shape)
model.add(Lambda(antirectifier,
output_shape=antirectifier_output_shape))
Arguments
- function: The function to be evaluated. Takes input tensor as first argument.
- output_shape: Expected output shape from function.
Only relevant when using Theano.
Can be a tuple or function.
If a tuple, it only specifies the first dimension onward;
sample dimension is assumed either the same as the input:
output_shape = (input_shape[0], ) + output_shape
or, the input isNone
and the sample dimension is alsoNone
:output_shape = (None, ) + output_shape
If a function, it specifies the entire shape as a function of the input shape:output_shape = f(input_shape)
- arguments: optional dictionary of keyword arguments to be passed to the function.
Input shape
Arbitrary. Use the keyword argument input_shape (tuple of integers, does not include the samples axis) when using this layer as the first layer in a model.
Output shape
Specified by output_shape
argument
(or auto-inferred when using TensorFlow).
ActivityRegularization
keras.layers.ActivityRegularization(l1=0.0, l2=0.0)
Layer that applies an update to the cost function based input activity.
Arguments
- l1: L1 regularization factor (positive float).
- l2: L2 regularization factor (positive float).
Input shape
Arbitrary. Use the keyword argument input_shape
(tuple of integers, does not include the samples axis)
when using this layer as the first layer in a model.
Output shape
Same shape as input.
Masking
keras.layers.Masking(mask_value=0.0)
Masks a sequence by using a mask value to skip timesteps.
For each timestep in the input tensor (dimension #1 in the tensor),
if all values in the input tensor at that timestep
are equal to mask_value
, then the timestep will be masked (skipped)
in all downstream layers (as long as they support masking).
If any downstream layer does not support masking yet receives such an input mask, an exception will be raised.
Example
Consider a Numpy data array x
of shape (samples, timesteps, features)
,
to be fed to an LSTM layer.
You want to mask timestep #3 and #5 because you lack data for
these timesteps. You can:
- set
x[:, 3, :] = 0.
andx[:, 5, :] = 0.
- insert a
Masking
layer withmask_value=0.
before the LSTM layer:
model = Sequential()
model.add(Masking(mask_value=0., input_shape=(timesteps, features)))
model.add(LSTM(32))