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arqcenick · Dec 22, 2017 · 27a5b85 · 27a5b85
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diff --git a/FikraRegresser.ipynb b/FikraRegresser.ipynb
diff --git a/GenerateWordEmbeddings-Freq.ipynb b/GenerateWordEmbeddings-Freq.ipynb
diff --git a/GenerateWordEmbeddings.ipynb b/GenerateWordEmbeddings.ipynb
diff --git a/NaiveRegress.ipynb b/NaiveRegress.ipynb
diff --git a/data/train.txt b/data/train.txt
diff --git a/data/valid.txt b/data/valid.txt
diff --git a/data_bak/data/test.txt b/data_bak/data/test.txt
diff --git a/data_bak/data/train.txt b/data_bak/data/train.txt
diff --git a/data_bak/data/valid.txt b/data_bak/data/valid.txt
diff --git a/generate.py b/generate.py
@@ -1,117 +1,128 @@
-from __future__ import absolute_import
-from __future__ import division
-from __future__ import print_function
-
-import os
-import time
-import numpy as np
-import tensorflow as tf
-
-import model
-from data_reader import load_data, DataReader
-
-
-flags = tf.flags
-
-# data
-flags.DEFINE_string('load_model',   None,    'filename of the model to load')
-# we need data only to compute vocabulary
-flags.DEFINE_string('data_dir',   'data',    'data directory')
-flags.DEFINE_integer('num_samples', 300, 'how many words to generate')
-flags.DEFINE_float('temperature', 1.0, 'sampling temperature')
-
-# model params
-flags.DEFINE_integer('rnn_size',        650,                            'size of LSTM internal state')
-flags.DEFINE_integer('highway_layers',  2,                              'number of highway layers')
-flags.DEFINE_integer('char_embed_size', 15,                             'dimensionality of character embeddings')
-flags.DEFINE_string ('kernels',         '[1,2,3,4,5,6,7]',              'CNN kernel widths')
-flags.DEFINE_string ('kernel_features', '[50,100,150,200,200,200,200]', 'number of features in the CNN kernel')
-flags.DEFINE_integer('rnn_layers',      2,                              'number of layers in the LSTM')
-flags.DEFINE_float  ('dropout',         0.5,                            'dropout. 0 = no dropout')
-
-# optimization
-flags.DEFINE_integer('max_word_length',     65,   'maximum word length')
-
-# bookkeeping
-flags.DEFINE_integer('seed',           3435, 'random number generator seed')
-flags.DEFINE_string ('EOS',            '+',  '<EOS> symbol. should be a single unused character (like +) for PTB and blank for others')
-
-FLAGS = flags.FLAGS
-
-
-def main(_):
-    ''' Loads trained model and evaluates it on test split '''
-
-    if FLAGS.load_model is None:
-        print('Please specify checkpoint file to load model from')
-        return -1
-
-    if not os.path.exists(FLAGS.load_model + '.meta'):
-        print('Checkpoint file not found', FLAGS.load_model)
-        return -1
-
-    word_vocab, char_vocab, word_tensors, char_tensors, max_word_length = \
-        load_data(FLAGS.data_dir, FLAGS.max_word_length, eos=FLAGS.EOS)
-
-    print('initialized test dataset reader')
-
-    with tf.Graph().as_default(), tf.Session() as session:
-
-        # tensorflow seed must be inside graph
-        tf.set_random_seed(FLAGS.seed)
-        np.random.seed(seed=FLAGS.seed)
-
-        ''' build inference graph '''
-        with tf.variable_scope("Model"):
-            m = model.inference_graph(
-                    char_vocab_size=char_vocab.size,
-                    word_vocab_size=word_vocab.size,
-                    char_embed_size=FLAGS.char_embed_size,
-                    batch_size=1,
-                    num_highway_layers=FLAGS.highway_layers,
-                    num_rnn_layers=FLAGS.rnn_layers,
-                    rnn_size=FLAGS.rnn_size,
-                    max_word_length=max_word_length,
-                    kernels=eval(FLAGS.kernels),
-                    kernel_features=eval(FLAGS.kernel_features),
-                    num_unroll_steps=1,
-                    dropout=0)
-
-            # we need global step only because we want to read it from the model
-            global_step = tf.Variable(0, dtype=tf.int32, name='global_step')
-
-        saver = tf.train.Saver()
-        saver.restore(session, FLAGS.load_model)
-        print('Loaded model from', FLAGS.load_model, 'saved at global step', global_step.eval())
-
-        ''' training starts here '''
-        rnn_state = session.run(m.initial_rnn_state)
-        logits = np.ones((word_vocab.size,))
-        rnn_state = session.run(m.initial_rnn_state)
-        for i in range(FLAGS.num_samples):
-            logits = logits / FLAGS.temperature
-            prob = np.exp(logits)
-            prob /= np.sum(prob)
-            prob = prob.ravel()
-            ix = np.random.choice(range(len(prob)), p=prob)
-
-            word = word_vocab.token(ix)
-            if word == '|':  # EOS
-                print('<unk>', end=' ')
-            elif word == '+':
-                print('\n')
-            else:
-                print(word, end=' ')
-
-            char_input = np.zeros((1, 1, max_word_length))
-            for i,c in enumerate('{' + word + '}'):
-                char_input[0,0,i] = char_vocab[c]
-
-            logits, rnn_state = session.run([m.logits, m.final_rnn_state],
-                                         {m.input: char_input,
-                                          m.initial_rnn_state: rnn_state})
-            logits = np.array(logits)
-
-
-if __name__ == "__main__":
-    tf.app.run()
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import time
+import numpy as np
+import tensorflow as tf
+
+import model
+from data_reader import load_data, DataReader
+
+os.environ["CUDA_VISIBLE_DEVICES"]="0"
+
+
+flags = tf.flags
+
+# data
+flags.DEFINE_string('load_model',   None,    'filename of the model to load')
+# we need data only to compute vocabulary
+flags.DEFINE_string('data_dir',   'data',    'data directory')
+flags.DEFINE_integer('num_samples', 300, 'how many words to generate')
+flags.DEFINE_float('temperature', 1.0, 'sampling temperature')
+
+# model params
+flags.DEFINE_integer('rnn_size',        650,                            'size of LSTM internal state')
+flags.DEFINE_integer('highway_layers',  2,                              'number of highway layers')
+flags.DEFINE_integer('char_embed_size', 15,                             'dimensionality of character embeddings')
+flags.DEFINE_string ('kernels',         '[1,2,3,4,5,6,7]',              'CNN kernel widths')
+flags.DEFINE_string ('kernel_features', '[50,100,150,200,200,200,200]', 'number of features in the CNN kernel')
+flags.DEFINE_integer('rnn_layers',      2,                              'number of layers in the LSTM')
+flags.DEFINE_float  ('dropout',         0.5,                            'dropout. 0 = no dropout')
+
+# optimization
+flags.DEFINE_integer('max_word_length',     65,   'maximum word length')
+
+# bookkeeping
+flags.DEFINE_integer('seed',           3435, 'random number generator seed')
+flags.DEFINE_string ('EOS',            '+',  '<EOS> symbol. should be a single unused character (like +) for PTB and blank for others')
+
+FLAGS = flags.FLAGS
+
+
+def main(_):
+    ''' Loads trained model and evaluates it on test split '''
+
+    if FLAGS.load_model is None:
+        print('Please specify checkpoint file to load model from')
+        return -1
+
+    if not os.path.exists(FLAGS.load_model + '.meta'):
+        print('Checkpoint file not found', FLAGS.load_model)
+        return -1
+
+    word_vocab, char_vocab, word_tensors, char_tensors, max_word_length = \
+        load_data(FLAGS.data_dir, FLAGS.max_word_length, eos=FLAGS.EOS)
+
+    print('initialized test dataset reader')
+
+    with tf.Graph().as_default(), tf.Session() as session:
+
+        # tensorflow seed must be inside graph
+        tf.set_random_seed(FLAGS.seed)
+        np.random.seed(seed=FLAGS.seed)
+
+        ''' build inference graph '''
+        with tf.variable_scope("Model"):
+            m = model.inference_graph(
+                    char_vocab_size=char_vocab.size,
+                    word_vocab_size=word_vocab.size,
+                    char_embed_size=FLAGS.char_embed_size,
+                    batch_size=1,
+                    num_highway_layers=FLAGS.highway_layers,
+                    num_rnn_layers=FLAGS.rnn_layers,
+                    rnn_size=FLAGS.rnn_size,
+                    max_word_length=max_word_length,
+                    kernels=eval(FLAGS.kernels),
+                    kernel_features=eval(FLAGS.kernel_features),
+                    num_unroll_steps=1,
+                    dropout=0)
+
+            # we need global step only because we want to read it from the model
+            global_step = tf.Variable(0, dtype=tf.int32, name='global_step')
+
+        saver = tf.train.Saver()
+        saver.restore(session, FLAGS.load_model)
+        print('Loaded model from', FLAGS.load_model, 'saved at global step', global_step.eval())
+
+        ''' training starts here '''
+        rnn_state = session.run(m.initial_rnn_state)
+        logits = np.ones((word_vocab.size,))
+        rnn_state = session.run(m.initial_rnn_state)
+        for i in range(FLAGS.num_samples):
+            if i > 0:
+                logits = logits / FLAGS.temperature
+                prob = np.exp(logits)
+                prob /= np.sum(prob)
+                prob = prob.ravel()
+
+                ix = np.random.choice(range(len(prob)), p=prob)
+                word = word_vocab.token(ix)
+            else:
+                word_input = input("Enter the first word:")
+                word = word_vocab.token(word_vocab.get(word_input))
+
+
+            if word == '|':  # EOS
+                print('<unk>', end=' ')
+            elif word == '+':
+                print('\n')
+            elif word == '#':
+                print('\nFıkra is generated')
+                break
+            else:
+                print(word, end=' ')
+
+            char_input = np.zeros((1, 1, max_word_length))
+            for i,c in enumerate('{' + word + '}'):
+                char_input[0,0,i] = char_vocab[c]
+
+            logits, rnn_state = session.run([m.logits, m.final_rnn_state],
+                                         {m.input: char_input,
+                                          m.initial_rnn_state: rnn_state})
+            logits = np.array(logits)
+
+
+if __name__ == "__main__":
+    tf.app.run()
diff --git a/generate_wordembeddings.py b/generate_wordembeddings.py
@@ -0,0 +1,110 @@
+from __future__ import absolute_import
+from __future__ import division
+from __future__ import print_function
+
+import os
+import time
+import numpy as np
+import tensorflow as tf
+
+import model
+from data_reader import load_data, DataReader
+
+os.environ["CUDA_VISIBLE_DEVICES"]="0"
+
+
+flags = tf.flags
+
+# data
+flags.DEFINE_string('load_model',   None,    'filename of the model to load')
+# we need data only to compute vocabulary
+flags.DEFINE_string('data_dir',   'data',    'data directory')
+flags.DEFINE_integer('num_samples', 300, 'how many words to generate')
+flags.DEFINE_float('temperature', 1.0, 'sampling temperature')
+
+# model params
+flags.DEFINE_integer('rnn_size',        650,                            'size of LSTM internal state')
+flags.DEFINE_integer('highway_layers',  2,                              'number of highway layers')
+flags.DEFINE_integer('char_embed_size', 15,                             'dimensionality of character embeddings')
+flags.DEFINE_string ('kernels',         '[1,2,3,4,5,6,7]',              'CNN kernel widths')
+flags.DEFINE_string ('kernel_features', '[50,100,150,200,200,200,200]', 'number of features in the CNN kernel')
+flags.DEFINE_integer('rnn_layers',      2,                              'number of layers in the LSTM')
+flags.DEFINE_float  ('dropout',         0.5,                            'dropout. 0 = no dropout')
+
+# optimization
+flags.DEFINE_integer('max_word_length',     65,   'maximum word length')
+
+# bookkeeping
+flags.DEFINE_integer('seed',           3435, 'random number generator seed')
+flags.DEFINE_string ('EOS',            '+',  '<EOS> symbol. should be a single unused character (like +) for PTB and blank for others')
+
+FLAGS = flags.FLAGS
+
+
+def main(_):
+    ''' Loads trained model and evaluates it on test split '''
+
+    if FLAGS.load_model is None:
+        print('Please specify checkpoint file to load model from')
+        return -1
+
+    if not os.path.exists(FLAGS.load_model + '.meta'):
+        print('Checkpoint file not found', FLAGS.load_model)
+        return -1
+
+    word_vocab, char_vocab, word_tensors, char_tensors, max_word_length = \
+        load_data(FLAGS.data_dir, FLAGS.max_word_length, eos=FLAGS.EOS)
+
+    print('initialized test dataset reader')
+
+    with tf.Graph().as_default(), tf.Session() as session:
+
+        # tensorflow seed must be inside graph
+        tf.set_random_seed(FLAGS.seed)
+        np.random.seed(seed=FLAGS.seed)
+
+        ''' build inference graph '''
+        outputs_list = []
+        with tf.variable_scope("Model"):
+            m = model.inference_graph(
+                    char_vocab_size=char_vocab.size,
+                    word_vocab_size=word_vocab.size,
+                    char_embed_size=FLAGS.char_embed_size,
+                    batch_size=1,
+                    num_highway_layers=FLAGS.highway_layers,
+                    num_rnn_layers=FLAGS.rnn_layers,
+                    rnn_size=FLAGS.rnn_size,
+                    max_word_length=max_word_length,
+                    kernels=eval(FLAGS.kernels),
+                    kernel_features=eval(FLAGS.kernel_features),
+                    num_unroll_steps=1,
+                    dropout=0)
+
+            # we need global step only because we want to read it from the model
+            global_step = tf.Variable(0, dtype=tf.int32, name='global_step')
+
+        saver = tf.train.Saver()
+        saver.restore(session, FLAGS.load_model)
+        print('Loaded model from', FLAGS.load_model, 'saved at global step', global_step.eval())
+
+        ''' training starts here '''
+        rnn_state = session.run(m.initial_rnn_state)
+        logits = np.ones((word_vocab.size,))
+        rnn_state = session.run(m.initial_rnn_state)
+        for i in range(5):
+
+            word = word_vocab.token(i)
+            char_input = np.zeros((1, 1, max_word_length))
+            for i,c in enumerate('{' + word + '}'):
+                char_input[0,0,i] = char_vocab[c]
+
+            outputs, logits = session.run([m.rnn_outputs,m.logits],
+                                         {m.input: char_input,
+                                          m.initial_rnn_state: rnn_state})
+            outputs_list.append(outputs)
+
+        outputs_array = np.array(outputs_list)
+        print(outputs_array.shape)
+
+if __name__ == "__main__":
+    tf.app.run()
diff --git a/train.py b/train.py
@@ -10,6 +10,7 @@
 import model
 from data_reader import load_data, DataReader
 
+os.environ["CUDA_VISIBLE_DEVICES"]="0"
 
 flags = tf.flags
 

diff --git a/x_test.npz b/x_test.npz