In the Python file, you will be creating a neural network to predict the price of a stock. Part of the
program is already implemented, your goal is to set up the model, train the model on the training
data set, then finally predict the values from the testing data set. You should create a Sequential
model with 5 layers. Your model should use 2 LSTM layers, and after each use a Dropout layer
with a rate of 0.2. The first LSTM layer should have input_shape as a parameter along with units
set to 4. The final layer should be a Dense layer with units set to 1. Once the model is set up, you
should compile it with a mean_squared_error loss and the optimizer set to adam. When you fit the
training dataset, make sure to set the following values: epochs=5, batch_size=16, verbose=0
Finally, you should run the predict function on the x_test dataset and print the last column of the
array along with the model summary..
In the Python file you will be creating a neural network to.pdf
1. In the Python file, you will be creating a neural network to predict the price of a stock. Part of the
program is already implemented, your goal is to set up the model, train the model on the training
data set, then finally predict the values from the testing data set. You should create a Sequential
model with 5 layers. Your model should use 2 LSTM layers, and after each use a Dropout layer
with a rate of 0.2. The first LSTM layer should have input_shape as a parameter along with units
set to 4. The final layer should be a Dense layer with units set to 1. Once the model is set up, you
should compile it with a mean_squared_error loss and the optimizer set to adam. When you fit the
training dataset, make sure to set the following values: epochs=5, batch_size=16, verbose=0
Finally, you should run the predict function on the x_test dataset and print the last column of the
array along with the model summary.
