Added fake_epoch for observation
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@ -2,7 +2,9 @@
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@ -32,8 +34,8 @@
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@ -135,6 +137,7 @@
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<find>split_number</find>
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<find>StringIO</find>
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<find>shuffle_inputs</find>
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<find>generator</find>
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@ -178,7 +181,7 @@
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@ -860,8 +823,8 @@
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<entry file="file://$PROJECT_DIR$/cnn/word_accetuation/cnn_dictionary/cnn_test_on_other_attributes.ipynb" />
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@ -928,12 +891,6 @@
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<entry file="file://$PROJECT_DIR$/cnn/word_accetuation/cnn_dictionary/character_based_ffnn_keras.ipynb">
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@ -7,6 +7,7 @@ import h5py
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import gc
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import math
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# functions for saving, loading and shuffling whole arrays to ram
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def save_inputs(file_name, X, y, other_features=[]):
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h5f = h5py.File(file_name, 'w')
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@ -18,6 +19,7 @@ def save_inputs(file_name, X, y, other_features=[]):
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h5f.create_dataset(k, data=v)
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h5f.close()
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def load_inputs(file_name, other_features=False):
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h5f = h5py.File(file_name,'r')
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X = h5f['X'][:]
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@ -42,6 +44,7 @@ def shuffle_inputs(X, y, X_pure=[]):
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else:
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return X, y
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# functions for saving and loading partial arrays to ram
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def create_and_save_inputs(file_name, part, X, y, X_pure):
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# X, y, X_pure = generate_full_vowel_matrix_inputs()
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@ -51,6 +54,7 @@ def create_and_save_inputs(file_name, part, X, y, X_pure):
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h5f.create_dataset(k,data=v)
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h5f.close()
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def load_extended_inputs(file_name, obtain_range):
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h5f = h5py.File(file_name,'r')
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X = h5f['X'][obtain_range[0]:obtain_range[1]]
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@ -70,6 +74,7 @@ def create_and_save_shuffle_vector(file_name, shuffle_vector):
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h5f.create_dataset(k,data=v)
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h5f.close()
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def load_shuffle_vector(file_name):
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h5f = h5py.File(file_name,'r')
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shuffle_vector = h5f['shuffle_vector'][[179859, 385513, 893430]]
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@ -452,6 +457,19 @@ def generate_X_and_y_RAM_efficient(name, split_number):
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h5f.close()
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# generator for inputs for tracking of data fitting
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def generate_fake_epoch(orig_X, orig_X_additional, orig_y, batch_size):
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size = orig_X.shape[0]
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while 1:
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loc = 0
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while loc < size:
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if loc + batch_size >= size:
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yield([orig_X[loc:size], orig_X_additional[loc:size]], orig_y[loc:size])
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else:
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yield([orig_X[loc:loc + batch_size], orig_X_additional[loc:loc + batch_size]], orig_y[loc:loc + batch_size])
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loc += batch_size
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# generator for inputs
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def generate_arrays_from_file(path, batch_size):
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h5f = h5py.File(path, 'r')
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