Create course_7_shakespeare_gen.py

Author: wiibrew

course_7_shakespeare_gen.py

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+from __future__ import absolute_import, division, print_function
+
+import os
+import pickle
+from six.moves import urllib
+
+import tflearn
+from tflearn.data_utils import *
+
+path = "shakespeare_input.txt"
+char_idx_file = 'char_idx.pickle'
+
+if not os.path.isfile(path):
+    urllib.request.urlretrieve("https://raw.githubusercontent.com/tflearn/tflearn.github.io/master/resources/shakespeare_input.txt", path)
+
+maxlen = 25
+
+char_idx = None
+if os.path.isfile(char_idx_file):
+  print('Loading previous char_idx')
+  char_idx = pickle.load(open(char_idx_file, 'rb'))
+
+X, Y, char_idx = \
+    textfile_to_semi_redundant_sequences(path, seq_maxlen=maxlen, redun_step=3,
+                                         pre_defined_char_idx=char_idx)
+
+pickle.dump(char_idx, open(char_idx_file,'wb'))
+
+g = tflearn.input_data([None, maxlen, len(char_idx)])
+g = tflearn.lstm(g, 512, return_seq=True)
+g = tflearn.dropout(g, 0.5)
+g = tflearn.lstm(g, 512, return_seq=True)
+g = tflearn.dropout(g, 0.5)
+g = tflearn.lstm(g, 512)
+g = tflearn.dropout(g, 0.5)
+g = tflearn.fully_connected(g, len(char_idx), activation='softmax')
+g = tflearn.regression(g, optimizer='adam', loss='categorical_crossentropy',
+                       learning_rate=0.001)
+
+m = tflearn.SequenceGenerator(g, dictionary=char_idx,
+                              seq_maxlen=maxlen,
+                              clip_gradients=5.0,
+                              checkpoint_path='model_shakespeare')
+
+for i in range(50):
+    seed = random_sequence_from_textfile(path, maxlen)
+    m.fit(X, Y, validation_set=0.1, batch_size=128,
+          n_epoch=1, run_id='shakespeare')
+    print("-- TESTING...")
+    print("-- Test with temperature of 1.0 --")
+    print(m.generate(600, temperature=1.0, seq_seed=seed))
+    print("-- Test with temperature of 0.5 --")
+    print(m.generate(600, temperature=0.5, seq_seed=seed))