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simplelstm.py

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+# LSTM for sequence classification in the IMDB dataset
+import tensorflow as tf
+from tensorflow.keras.datasets import imdb
+from tensorflow.keras.models import Sequential
+from tensorflow.keras.layers import Dense
+from tensorflow.keras.layers import LSTM
+from tensorflow.keras.layers import Embedding
+from tensorflow.keras.preprocessing import sequence
+
+# fix random seed for reproducibility
+tf.random.set_seed(7)
+# load the dataset but only keep the top n words, zero the rest
+top_words = 5000
+(X_train, y_train), (X_test, y_test) = imdb.load_data(num_words=top_words)
+# truncate and pad input sequences
+max_review_length = 500
+X_train = sequence.pad_sequences(X_train, maxlen=max_review_length)
+X_test = sequence.pad_sequences(X_test, maxlen=max_review_length)
+# create the model
+embedding_vecor_length = 32
+model = Sequential()
+model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))
+model.add(LSTM(200))
+model.add(Dense(1, activation='sigmoid'))
+model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+print(model.summary())
+model.fit(X_train, y_train, epochs=20, batch_size=64)
+# Final evaluation of the model
+scores = model.evaluate(X_test, y_test, verbose=0)
+print("Accuracy: %.2f%%" % (scores[1]*100))
+
+model.save(r'C:\Users\shahi\Desktop\My Projects\DeepPredictorHub\LS.keras')