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@ -435,8 +437,8 @@
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10
train.py
10
train.py
@ -61,20 +61,16 @@ def invert_scale(scaler, X, yhat):
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# fit an LSTM network to training data
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def fit_lstm(train, batch_size2, nb_epoch, neurons):
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print(train)
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X, y = train[:, 0:-1], train[:, -1]
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X = X.reshape(X.shape[0], 1, X.shape[1])
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print(X, y)
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model = Sequential()
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model.add(LSTM(neurons, batch_input_shape=(batch_size2, X.shape[1], X.shape[2]), stateful=True))
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model.add(Dense(1))
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model.compile(loss='mean_squared_error', optimizer=Adam(lr=0.01))
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model.compile(loss='mean_squared_error', optimizer='adam')
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for i in range(nb_epoch):
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print("Epoch {}/{}".format(i, nb_epoch))
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model.fit(X, y, epochs=1, batch_size=batch_size2, verbose=0, shuffle=False)
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#loss, accuracy = model.evaluate(X, y)
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print(model.evaluate(X, y))
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#print(loss, accuracy)
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loss = model.evaluate(X, y)
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print("Epoch {}/{}, loss = {}".format(i, nb_epoch, loss))
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model.reset_states()
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return model
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