train the model and get the predictions

train.py

#build LSTM model
model = Sequential()
model.add(LSTM(50, return_sequences=True, input_shape=(x_train.shape[1],1)))
model.add(LSTM(50,return_sequences=False))

model.add(Dense(25))
model.add(Dense(1))

#compile the model
model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])

#train the model
model.fit(x_train, y_train, batch_size=1, epochs=5)

#create test dataset
test_data = scaled_data[train_data_len-60:, :]

#create dataset x_test, y_test
x_test = []
y_test = dataset[train_data_len:, :]
for i in range(60,len(test_data)):
  x_test.append(test_data[i-60:i, 0])

#convert data to numpy array
x_test = np.array(x_test)

#reshape the data
x_test = np.reshape(x_test, (x_test.shape[0], x_test.shape[1], 1))

#get the models predicted price values
predictions = model.predict(x_test)
predictions = scaler.inverse_transform(predictions)