HenryAI/KerasAPIReference.txt · Datasets at Hugging Face

text stringlengths 0 1.46k
input_length=None,
time_major=False,
zero_output_for_mask=False,
)
Iterates over the time dimension of a tensor.

Arguments

step_function: RNN step function. Args; input; Tensor with shape (samples, ...) (no time dimension), representing input for the batch of samples at a certain time step. states; List of tensors. Returns; output; Tensor with shape (samples, output_dim) (no time dimension). new_states; List of tensors, same length and shapes as 'states'. The first state in the list must be the output tensor at the previous timestep.
inputs: Tensor of temporal data of shape (samples, time, ...) (at least 3D), or nested tensors, and each of which has shape (samples, time, ...).
initial_states: Tensor with shape (samples, state_size) (no time dimension), containing the initial values for the states used in the step function. In the case that state_size is in a nested shape, the shape of initial_states will also follow the nested structure.
go_backwards: Boolean. If True, do the iteration over the time dimension in reverse order and return the reversed sequence.
mask: Binary tensor with shape (samples, time, 1), with a zero for every element that is masked.
constants: List of constant values passed at each step.
unroll: Whether to unroll the RNN or to use a symbolic while_loop.
input_length: An integer or a 1-D Tensor, depending on whether the time dimension is fixed-length or not. In case of variable length input, it is used for masking in case there's no mask specified.
time_major: Boolean. If true, the inputs and outputs will be in shape (timesteps, batch, ...), whereas in the False case, it will be (batch, timesteps, ...). Using time_major = True is a bit more efficient because it avoids transposes at the beginning and end of the RNN calculation. However, most TensorFlow data is batch-major, so by default this function accepts input and emits output in batch-major form.
zero_output_for_mask: Boolean. If True, the output for masked timestep will be zeros, whereas in the False case, output from previous timestep is returned.
Returns

A tuple, (last_output, outputs, new_states). last_output: the latest output of the rnn, of shape (samples, ...) outputs: tensor with shape (samples, time, ...) where each entry outputs[s, t] is the output of the step function at time t for sample s. new_states: list of tensors, latest states returned by the step function, of shape (samples, ...).

Raises

ValueError: if input dimension is less than 3.
ValueError: if unroll is True but input timestep is not a fixed number.
ValueError: if mask is provided (not None) but states is not provided (len(states) == 0).
Model plotting utilities
plot_model function
tf.keras.utils.plot_model(
model,
to_file="model.png",
show_shapes=False,
show_dtype=False,
show_layer_names=True,
rankdir="TB",
expand_nested=False,
dpi=96,
)
Converts a Keras model to dot format and save to a file.

Example

input = tf.keras.Input(shape=(100,), dtype='int32', name='input')
x = tf.keras.layers.Embedding(
output_dim=512, input_dim=10000, input_length=100)(input)
x = tf.keras.layers.LSTM(32)(x)
x = tf.keras.layers.Dense(64, activation='relu')(x)
x = tf.keras.layers.Dense(64, activation='relu')(x)
x = tf.keras.layers.Dense(64, activation='relu')(x)
output = tf.keras.layers.Dense(1, activation='sigmoid', name='output')(x)
model = tf.keras.Model(inputs=[input], outputs=[output])
dot_img_file = '/tmp/model_1.png'
tf.keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True)
Arguments

model: A Keras model instance
to_file: File name of the plot image.
show_shapes: whether to display shape information.
show_dtype: whether to display layer dtypes.
show_layer_names: whether to display layer names.
rankdir: rankdir argument passed to PyDot, a string specifying the format of the plot: 'TB' creates a vertical plot; 'LR' creates a horizontal plot.
expand_nested: Whether to expand nested models into clusters.
dpi: Dots per inch.
Returns

A Jupyter notebook Image object if Jupyter is installed. This enables in-line display of the model plots in notebooks.

model_to_dot function
tf.keras.utils.model_to_dot(
model,
show_shapes=False,
show_dtype=False,
show_layer_names=True,
rankdir="TB",
expand_nested=False,
dpi=96,
subgraph=False,
)
Convert a Keras model to dot format.

Arguments

model: A Keras model instance.
show_shapes: whether to display shape information.
show_dtype: whether to display layer dtypes.
show_layer_names: whether to display layer names.
rankdir: rankdir argument passed to PyDot, a string specifying the format of the plot: 'TB' creates a vertical plot; 'LR' creates a horizontal plot.
expand_nested: whether to expand nested models into clusters.
dpi: Dots per inch.
subgraph: whether to return a pydot.Cluster instance.
Returns

A pydot.Dot instance representing the Keras model or a pydot.Cluster instance representing nested model if subgraph=True.

Raises

ImportError: if graphviz or pydot are not available.Serialization utilities
CustomObjectScope class
tf.keras.utils.custom_object_scope(*args)

input_length=None,

time_major=False,

zero_output_for_mask=False,

)

Iterates over the time dimension of a tensor.

Arguments

step_function: RNN step function. Args; input; Tensor with shape (samples, ...) (no time dimension), representing input for the batch of samples at a certain time step. states; List of tensors. Returns; output; Tensor with shape (samples, output_dim) (no time dimension). new_states; List of tensors, same length and shapes as 'states'. The first state in the list must be the output tensor at the previous timestep.

inputs: Tensor of temporal data of shape (samples, time, ...) (at least 3D), or nested tensors, and each of which has shape (samples, time, ...).

initial_states: Tensor with shape (samples, state_size) (no time dimension), containing the initial values for the states used in the step function. In the case that state_size is in a nested shape, the shape of initial_states will also follow the nested structure.

go_backwards: Boolean. If True, do the iteration over the time dimension in reverse order and return the reversed sequence.

mask: Binary tensor with shape (samples, time, 1), with a zero for every element that is masked.

constants: List of constant values passed at each step.

unroll: Whether to unroll the RNN or to use a symbolic while_loop.

input_length: An integer or a 1-D Tensor, depending on whether the time dimension is fixed-length or not. In case of variable length input, it is used for masking in case there's no mask specified.

time_major: Boolean. If true, the inputs and outputs will be in shape (timesteps, batch, ...), whereas in the False case, it will be (batch, timesteps, ...). Using time_major = True is a bit more efficient because it avoids transposes at the beginning and end of the RNN calculation. However, most TensorFlow data is batch-major, so by default this function accepts input and emits output in batch-major form.

zero_output_for_mask: Boolean. If True, the output for masked timestep will be zeros, whereas in the False case, output from previous timestep is returned.

Returns

A tuple, (last_output, outputs, new_states). last_output: the latest output of the rnn, of shape (samples, ...) outputs: tensor with shape (samples, time, ...) where each entry outputs[s, t] is the output of the step function at time t for sample s. new_states: list of tensors, latest states returned by the step function, of shape (samples, ...).

Raises

ValueError: if input dimension is less than 3.

ValueError: if unroll is True but input timestep is not a fixed number.

ValueError: if mask is provided (not None) but states is not provided (len(states) == 0).

Model plotting utilities

plot_model function

tf.keras.utils.plot_model(

model,

to_file="model.png",

show_shapes=False,

show_dtype=False,

show_layer_names=True,

rankdir="TB",

expand_nested=False,

dpi=96,

)

Converts a Keras model to dot format and save to a file.

Example

input = tf.keras.Input(shape=(100,), dtype='int32', name='input')

x = tf.keras.layers.Embedding(

output_dim=512, input_dim=10000, input_length=100)(input)

x = tf.keras.layers.LSTM(32)(x)

x = tf.keras.layers.Dense(64, activation='relu')(x)

output = tf.keras.layers.Dense(1, activation='sigmoid', name='output')(x)

model = tf.keras.Model(inputs=[input], outputs=[output])

dot_img_file = '/tmp/model_1.png'

tf.keras.utils.plot_model(model, to_file=dot_img_file, show_shapes=True)

Arguments

model: A Keras model instance

to_file: File name of the plot image.

show_shapes: whether to display shape information.

show_dtype: whether to display layer dtypes.

show_layer_names: whether to display layer names.

rankdir: rankdir argument passed to PyDot, a string specifying the format of the plot: 'TB' creates a vertical plot; 'LR' creates a horizontal plot.

expand_nested: Whether to expand nested models into clusters.

dpi: Dots per inch.

Returns

A Jupyter notebook Image object if Jupyter is installed. This enables in-line display of the model plots in notebooks.

model_to_dot function

tf.keras.utils.model_to_dot(

model,

show_shapes=False,

show_dtype=False,

show_layer_names=True,

rankdir="TB",

expand_nested=False,

dpi=96,

subgraph=False,

)

Convert a Keras model to dot format.

Arguments

model: A Keras model instance.

show_shapes: whether to display shape information.

show_dtype: whether to display layer dtypes.

show_layer_names: whether to display layer names.

rankdir: rankdir argument passed to PyDot, a string specifying the format of the plot: 'TB' creates a vertical plot; 'LR' creates a horizontal plot.

expand_nested: whether to expand nested models into clusters.

dpi: Dots per inch.

subgraph: whether to return a pydot.Cluster instance.

Returns

A pydot.Dot instance representing the Keras model or a pydot.Cluster instance representing nested model if subgraph=True.

Raises

ImportError: if graphviz or pydot are not available.Serialization utilities

CustomObjectScope class

tf.keras.utils.custom_object_scope(*args)