model = Sequential() model.add(Dense(300, activation = 'relu', input_shape = x_train.shape[1:])) model.add(Dense(100, activation = 'relu')) model.add(Dense(1)) model.compile(optimizer = 'adam', loss = 'mse', metrics = ['mae']) model.fit(x_train, y_train, epochs = 30, batch_size = 32, validation_split = 0.1) scores = model.evaluate(x_test, y_test, verbose = 0) predict = model.predict(x_test) scores = model.evaluate(x_test, y_test, verbose = 0) predict = model.predict(x_test) scores = model.evaluate(x_test, y_test, verbose = 0) predict = model.predict(x_test) scores = model.evaluate(x_test, y_test, verbose = 0) predict = model.predict(x_test) scores = model.evaluate(x_test, y_test, verbose = 0) predict = model.predict(x_test) scores = model.evaluate(x_test, y_test, verbose = 0) predict = model.predict(x_test)
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