ml for price forecasting rstudio

# Make Sessionnet = tf.Session()# Run initializernet.run(tf.global_variables_initializer())# Setup interactive plotplt.ion()fig = plt.figure()ax1 = fig.add_subplot(111)line1, = ax1.plot(y_test)line2, = ax1.plot(y_test*0.5)plt.show()# Number of epochs and batch sizeepochs = 10batch_size = 256for e in range(epochs):    # Shuffle training data    shuffle_indices = np.random.permutation(np.arange(len(y_train)))    X_train = X_train[shuffle_indices]    y_train = y_train[shuffle_indices]    # Minibatch training    for i in range(0, len(y_train) // batch_size):        start = i * batch_size        batch_x = X_train[start:start + batch_size]        batch_y = y_train[start:start + batch_size]        # Run optimizer with batch        net.run(opt, feed_dict={X: batch_x, Y: batch_y})        # Show progress        if np.mod(i, 5) == 0:            # Prediction            pred = net.run(out, feed_dict={X: X_test})            line2.set_ydata(pred)            plt.title('Epoch ' + str(e) + ', Batch ' + str(i))            file_name = 'img/epoch_' + str(e) + '_batch_' + str(i) + '.jpg'            plt.savefig(file_name)            plt.pause(0.01)# Print final MSE after Trainingmse_final = net.run(mse, feed_dict={X: X_test, Y: y_test})print(mse_final)