model.summary() model.compile(loss='categorical_crossentropy', optimizer='rmsprop', metrics=['accuracy']) checkpointer = ModelCheckpoint(filepath='mnist.model.best.hdf5',verbose=1, save_best_only=True) results = model.fit(X_train_processed_norm, y_train_encoded, batch_size=150, epochs=30, validation_split=0.33, callbacks=[checkpointer], verbose=0, shuffle=True) # Test accuracy # In[17]: model.load_weights('mnist.model.best.hdf5') score = model.evaluate(X_test_processed_norm, y_test_encoded, verbose=0) print('Test accuracy: %f' % score[1]) # ## Convolutional Neural Network # In[21]: from keras.layers import Conv2D, MaxPooling2D, GlobalAveragePooling2D # In[22]:
model.save_weights('model1.h5') import keras from keras.models import model_from_json from keras.preprocessing.text import Tokenizer from keras.preprocessing.sequence import pad_sequences import numpy as np twt = [ 'A lot of good things are happening. We are respected again throughout the world, and that\'s a great thing' ] json_file = open('model1.json', 'r') loaded_model_json = json_file.read() json_file.close() model = model_from_json(loaded_model_json) model.load_weights('model1.h5') max_fatures = 2000 tokenizer = Tokenizer(num_words=max_fatures, split=' ') tokenizer.fit_on_texts(twt) #vectorizing the tweet by the pre-fitted tokenizer instance twt = tokenizer.texts_to_sequences(twt) #padding the tweet to have exactly the same shape as `embedding_2` input twt = pad_sequences(twt, maxlen=28, dtype='int32', value=0) print(twt) sentiment = model.predict(twt, batch_size=1, verbose=2)[0] if (np.argmax(sentiment) == 0): print("negative") elif (np.argmax(sentiment) == 1):