8382_Gordiyenko_Alexandr_lb7

8382/Gordiyenko/lb/7/main.py

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+import numpy as np
+from keras.datasets import imdb
+from keras.models import Sequential
+from keras.layers import Dense
+from keras.layers import LSTM,Conv1D,MaxPooling1D,Dropout
+from keras.layers.embeddings import Embedding
+from keras.preprocessing import sequence
+import matplotlib.pyplot as plt
+
+top_words = 10000
+
+(training_data, training_targets), (testing_data, testing_targets) = imdb.load_data(num_words=10000)
+data = np.concatenate((training_data, testing_data), axis=0)
+targets = np.concatenate((training_targets, testing_targets), axis=0)
+
+max_review_length = 500
+training_data = sequence.pad_sequences(training_data, maxlen=max_review_length)
+testing_data = sequence.pad_sequences(testing_data, maxlen=max_review_length)
+embedding_vecor_length = 32
+
+def build_model_1():
+    model = Sequential()
+    model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))
+    model.add(Dropout(0.2))
+    model.add(LSTM(100))
+    model.add(Dropout(0.2))
+    model.add(Dense(1, activation='sigmoid'))
+    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+    return model
+
+def build_model_2():
+    model = Sequential()
+    model.add(Embedding(top_words, embedding_vecor_length, input_length=max_review_length))
+    model.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='relu'))
+    model.add(MaxPooling1D(pool_size=2))
+    model.add(Dropout(0.2))
+    model.add(LSTM(100))
+    model.add(Dropout(0.2))
+    model.add(Dense(1, activation='sigmoid'))
+    model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
+    return model
+
+def vectorize(sequences, dimension=500):
+    results = np.zeros((len(sequences), dimension))
+    for i, sequence in enumerate(sequences):
+        results[i, sequence] = 1
+    return results
+
+def final_pred(model_1,model_2,text):
+    pred_1 = model_1.predict(text)
+    pred_2 = model_2.predict(text)
+    result = 0.5*(pred_1+pred_2)
+    if result >= 0.5:
+        print('Отзыв хороший')
+    else:
+        print('Отзыв плохой')
+
+def evaluation(model_1,model_2,testing_data):
+    scores_1 = model_1.predict(testing_data)
+    scores_2 = model_2.predict(testing_data)
+    res = 0.5*(scores_1+scores_2)
+    res = np.round(res)
+    return res
+
+if __name__ == '__main__':
+    model_1 = build_model_1()
+    model_1.fit(training_data, training_targets, validation_data=(testing_data, testing_targets), epochs=3, batch_size=64)
+    scores_1 = model_1.evaluate(testing_data, testing_targets, verbose=0)
+    model_2 = build_model_2()
+    model_2.fit(training_data, training_targets, validation_data=(testing_data, testing_targets), epochs=3, batch_size=64)
+    scores_2 = model_2.evaluate(testing_data, testing_targets, verbose=0)
+    print("Accuracy: %.2f%%" % (scores_1[1] * 100))
+    print("Accuracy: %.2f%%" % (scores_2[1] * 100))
+    result = evaluation(model_1,model_2,testing_data)
+
+
+    text = "A brilliant masterpiece with unique style. It starts rather slow, \
+            but half time passed it burns the ground. It has everithing you want. \
+            Humour, Fantastic style, twisted plot, cool and memorable characters \
+            and action."
+    text_vec = [text[i] for i in range(len(text)) if i < 500]
+    final_pred(model_1,model_2,text_vec)