Exercicio Deep learning, Exercícios de Probabilidades e Processos Estocásticos

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25/11/2019 P2-Q 3.ipynb - Colaboratory from keras.models import Sequential from keras import layers from keras .optimizers import RMSprop from keras. layers import LSTM max features = 10000 maxlen = 500 batch size = 32 print('Loading data...') model.compile(optimizer='rmsprop', loss='binary crossentropy', metrics=['acc']) history = model.fit(input train, y train, epochs=10, batch size=128, validation split=0.2) model = Sequential() model.add(Embedding(max features, 32)) model .add(LSTM(32)) model.add(Dense(1, activation='sigmoid')) model.compile(optimizer='rmsprop', loss='binary crossentropy', metrics=['acc']) history = model.fit(input train, y train, epochs=10, batch size=128, validation split=0.2) acc = history.history['acc'] val acc = history.history['val acc'] loss = history.history['loss'] val loss = history.history['val loss'] epochs = range(1, len(acc) + 1) plt.title('Training and validation accuracy") plt.legend() plt.figure() plt.plot(epochs, loss, 'bo', label='Training loss') plt.plot(epochs, val loss, 'b', label='Validation loss") plt.title('Training and validation loss") plt.legend() plt.show() E https:/Icolab.research.google.comidrive/lidFdAeRJAy2atpCXD18Y rJH3USicodTitscroliTo=Onb7ijKR8ipi&printMode=true