Given a file name, read the file, retrieve the stories, and then convert the sentences into a single story. If max_length is supplied, any storie

Given a file name, read the file, retrieve the stories, and then convert the sentences into a single story. If max_length is supplied, any storie

Transpose dimensions. pattern should be a tuple or list of dimension indices, e.g. [0, 2, 1].

Call in a loop to create terminal progress bar @params: iteration - Required : current iteration (Int) total - Requi

Take a sequence (list of indexes of words), returns couples of [word_index, other_word index] and labels (1s or 0s), where la

Print out a summary of the model architecture, include parameter count information.

Transform each text in texts in a sequence of integers. Only top "nb_words" most frequent words will be taken into account.

Return a JSON string containing the model configuration. To load a model from a JSON save file, use `keras.models.from_json(json_stri

Return the tokens of a sentence including punctuation. >>> tokenize('Bob dropped the apple. Where is the apple?') ['Bob', 'dropped', 'the', '

Return the tokens of a sentence including punctuation. >>> tokenize('Bob dropped the apple. Where is the apple?') ['Bob', 'dropped', 'the', '

Instantiate a tensor variable.

Find and replace a single missing dimension in an output shape given and input shape. A near direct port of the internal numpy functi

Returns the pydot.Node corresponding to the given layer. `label` specify the name of the layer (only used if the layer isn't yet

Required for featurewise_center, featurewise_std_normalization and zca_whitening.

required before using sequences_to_matrix (if fit_on_texts was never called)

required before using texts_to_sequences or texts_to_matrix # Arguments texts: can be a list of strings,