Added embedding

Author: l2k2

keras-imdb/imdb-embedding.py

@@ -0,0 +1,75 @@
+# need to download glove from http://nlp.stanford.edu/data/glove.6B.zip
+# wget http://nlp.stanford.edu/data/glove.6B.zip
+# unzip http://nlp.stanford.edu/data/glove.6B.zip
+
+from keras.preprocessing import sequence
+from keras.models import Sequential
+from keras.layers import Dense, Dropout, Activation
+from keras.layers import Embedding, LSTM
+from keras.layers import Conv1D, Flatten
+from keras.datasets import imdb
+import wandb
+from wandb.keras import WandbCallback
+import imdb
+import numpy as np
+from keras.preprocessing import text
+
+wandb.init()
+config = wandb.config
+
+# set parameters:
+config.vocab_size = 1000
+config.maxlen = 300
+config.batch_size = 32
+config.embedding_dims = 50
+config.filters = 250
+config.kernel_size = 3
+config.hidden_dims = 100
+config.epochs = 10
+
+(X_train, y_train), (X_test, y_test) = imdb.load_imdb()
+
+tokenizer = text.Tokenizer(num_words=config.vocab_size)
+tokenizer.fit_on_texts(X_train)
+X_train = tokenizer.texts_to_matrix(X_train)
+X_test = tokenizer.texts_to_matrix(X_test)
+
+X_train = sequence.pad_sequences(X_train, maxlen=config.maxlen)
+X_test = sequence.pad_sequences(X_test, maxlen=config.maxlen)
+
+embeddings_index = dict()
+f = open('glove.6B.100d.txt')
+for line in f:
+    values = line.split()
+    word = values[0]
+    coefs = np.asarray(values[1:], dtype='float32')
+    embeddings_index[word] = coefs
+f.close()
+
+embedding_matrix = np.zeros((config.vocab_size, 100))
+for word, index in tokenizer.word_index.items():
+    if index > config.vocab_size - 1:
+        break
+    else:
+        embedding_vector = embeddings_index.get(word)
+        if embedding_vector is not None:
+            embedding_matrix[index] = embedding_vector
+
+
+## create model
+model = Sequential()
+model.add(Embedding(config.vocab_size, 100, input_length=config.maxlen, weights=[embedding_matrix], trainable=False))
+model.add(LSTM(config.hidden_dims, activation="sigmoid"))
+model.add(Dense(1, activation='sigmoid'))
+model.compile(loss='binary_crossentropy',
+              optimizer='rmsprop',
+              metrics=['accuracy'])
+
+model.fit(X_train, y_train,
+          batch_size=config.batch_size,
+          epochs=config.epochs,
+          validation_data=(X_test, y_test), callbacks=[WandbCallback()])
+
+
+
+