neuralmonkey package¶

Subpackages¶

Submodules¶

neuralmonkey.checking module¶

This module servers as a library of API checks used as assertions during constructing the computational graph.

exception neuralmonkey.checking.CheckingException¶: Bases: Exception

neuralmonkey.checking.assert_same_shape(tensor_a: tensorflow.python.framework.ops.Tensor, tensor_b: tensorflow.python.framework.ops.Tensor) → None¶: Check if two tensors have the same shape.

neuralmonkey.checking.assert_shape(tensor: tensorflow.python.framework.ops.Tensor, expected_shape: typing.List[typing.Union[int, NoneType]]) → None¶

Check shape of a tensor.

Parameters:	tensor – Tensor to be chcecked. expected_shape – Expected shape where None means the same as in TF and -1 means not checking the dimension.

neuralmonkey.checking.assert_type(obj, name, value, expected_type, can_be_none=False)¶

neuralmonkey.checking.check_dataset_and_coders(dataset, runners)¶

neuralmonkey.checking.missing_attributes(obj, attributes)¶

neuralmonkey.checking.type_to_str(type_obj)¶

neuralmonkey.dataset module¶

neuralmonkey.decoding_function module¶

Module which implements decoding functions using multiple attentions for RNN decoders.

See http://arxiv.org/abs/1606.07481

class neuralmonkey.decoding_function.Attention(attention_states, scope, input_weights=None, attention_fertility=None)¶

Bases: object

attention(query_state)¶: Put attention masks on att_states_reshaped using hidden_features and query.

get_logits(y)¶

class neuralmonkey.decoding_function.CoverageAttention(attention_states, scope, input_weights=None, attention_fertility=5)¶

Bases: neuralmonkey.decoding_function.Attention

get_logits(y)¶

neuralmonkey.decorators module¶

neuralmonkey.decorators.tensor(func)¶

neuralmonkey.functions module¶

neuralmonkey.functions.inverse_sigmoid_decay(param, rate, min_value=0.0, max_value=1.0, name=None, dtype=tf.float32)¶

Inverse sigmoid decay: k/(k+exp(x/k)).

The result will be scaled to the range (min_value, max_value).

Parameters:	param – The parameter x from the formula. rate – Non-negative k from the formula.

neuralmonkey.functions.piecewise_function(param, values, changepoints, name=None, dtype=tf.float32)¶

A piecewise function.

Parameters:	param – The function parameter. values – List of function values (numbers or tensors). changepoints – Sorted list of points where the function changes from one value to the next. Must be one item shorter than values.

neuralmonkey.learning_utils module¶

neuralmonkey.logging module¶

class neuralmonkey.logging.Logging¶

Bases: object

static debug(message, label=None)¶

debug_disabled = ['']¶

debug_enabled = ['none']¶

static log(message, color='yellow')¶: Logs message with a colored timestamp.

log_file = None¶

static log_print(text: str) → None¶: Prints a string both to console and a log file is it is defined.

static print_header(title)¶: Prints the title of the experiment and the set of arguments it uses.

static set_log_file(path)¶: Sets up the file where the logging will be done.

neuralmonkey.logging.debug(message, label=None)¶

neuralmonkey.logging.log(message, color='yellow')¶: Logs message with a colored timestamp.

neuralmonkey.logging.log_print(text: str) → None¶: Prints a string both to console and a log file is it is defined.

neuralmonkey.run module¶

neuralmonkey.server module¶

neuralmonkey.tf_manager module¶

neuralmonkey.tf_utils module¶

Small helper functions for TensorFlow.

neuralmonkey.tf_utils.gpu_memusage() → str¶

Return ‘’ or a string showing current GPU memory usage.

nvidia-smi result parsing based on https://github.com/wookayin/gpustat

neuralmonkey.tf_utils.has_gpu() → bool¶

Check if TensorFlow can access GPU.

The test is based on: https://github.com/tensorflow/tensorflow/blob/master/tensorflow/python/platform/test.py

...but we are interested only in CUDA GPU devices.

Returns:	True, if TF can access the GPU

neuralmonkey.train module¶

neuralmonkey.vocabulary module¶

Module contents¶

The neuralmonkey package is the root package of this project.