text: import deedee_ML_dataPull
  import deedee_ML_learn
  
  # Import
  import tensorflow as tf
  import numpy as np
  import pandas as pd
  from sklearn.preprocessing import MinMaxScaler
  import matplotlib.pyplot as plt
  
  # Pull the Data from the Database and create CSV files
  deedee_ML_dataPull.pull_data_and_create_csv()
  
  # Import data
  data = pd.read_csv('training_data/symbol_csvs/XSPA.csv')
  print(data)
  
  # Drop date variable
  # data = data.drop(['date'], 1)
  
  # Dimensions of dataset
  n = data.shape[0]
  p = data.shape[1]
  
  print("After date dims: ")
  print(data)
  print(n)
  print(p)
  
  # Make data a np.array
  data = data.values
  
  print("After date NP Array: ")
  print(data)
  
  # Training and test data
  train_start = 0
  train_end = int(np.floor(0.9*n))
  test_start = train_end + 1
  test_end = n
  data_train = data[np.arange(train_start, train_end), :]
  data_test = data[np.arange(test_start, test_end), :]
  
  # Scale data
  scaler = MinMaxScaler(feature_range=(-1, 1))
  scaler.fit(data_train)
  data_train = scaler.transform(data_train)
  data_test = scaler.transform(data_test)
  
  # Build X and y
  X_train = data_train[:, 1:]
  y_train = data_train[:, 0]
  X_test = data_test[:, 1:]
  y_test = data_test[:, 0]
  
  # Number of stocks in training data
  n_stocks = X_train.shape[1]
  
  # Neurons
  n_neurons_1 = 256
  n_neurons_2 = 128
  n_neurons_3 = 64
  n_neurons_4 = 32
  
  # Session
  net = tf.compat.v1.InteractiveSession()
  
  
  tf.compat.v1.disable_eager_execution()
  # Placeholder
  X = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None, n_stocks])
  Y = tf.compat.v1.placeholder(dtype=tf.float32, shape=[None])
  
  # Initializers
  sigma = 1
  weight_initializer = tf.compat.v1.variance_scaling_initializer(mode="fan_avg", distribution="uniform", scale=sigma)
  bias_initializer = tf.zeros_initializer()
  
  # Hidden weights
  W_hidden_1 = tf.Variable(weight_initializer([n_stocks, n_neurons_1]))
  bias_hidden_1 = tf.Variable(bias_initializer([n_neurons_1]))
  W_hidden_2 = tf.Variable(weight_initializer([n_neurons_1, n_neurons_2]))
  bias_hidden_2 = tf.Variable(bias_initializer([n_neurons_2]))
  W_hidden_3 = tf.Variable(weight_initializer([n_neurons_2, n_neurons_3]))
  bias_hidden_3 = tf.Variable(bias_initializer([n_neurons_3]))
  W_hidden_4 = tf.Variable(weight_initializer([n_neurons_3, n_neurons_4]))
  bias_hidden_4 = tf.Variable(bias_initializer([n_neurons_4]))
  
  # Output weights
  W_out = tf.Variable(weight_initializer([n_neurons_4, 1]))
  bias_out = tf.Variable(bias_initializer([1]))
  
  # Hidden layer
  hidden_1 = tf.nn.relu(tf.add(tf.matmul(X, W_hidden_1), bias_hidden_1))
  hidden_2 = tf.nn.relu(tf.add(tf.matmul(hidden_1, W_hidden_2), bias_hidden_2))
  hidden_3 = tf.nn.relu(tf.add(tf.matmul(hidden_2, W_hidden_3), bias_hidden_3))
  hidden_4 = tf.nn.relu(tf.add(tf.matmul(hidden_3, W_hidden_4), bias_hidden_4))
  
  # Output layer (transpose!)
  out = tf.transpose(tf.add(tf.matmul(hidden_4, W_out), bias_out))
  
  # Cost function
  mse = tf.reduce_mean(tf.compat.v1.squared_difference(out, Y))
  
  # Optimizer
  opt = tf.compat.v1.train.AdamOptimizer().minimize(mse)
  
  # Init
  net.run(tf.compat.v1.global_variables_initializer())
  
  # Setup plot
  plt.ion()
  fig = plt.figure()
  ax1 = fig.add_subplot(111)
  line1, = ax1.plot(y_test)
  line2, = ax1.plot(y_test * 0.5)
  plt.show()
  
  # Fit neural net
  batch_size = 64
  mse_train = []
  mse_test = []
  
  # Run
  epochs = 128
  for 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, 50) == 0:
              # MSE train and test
              mse_train.append(net.run(mse, feed_dict={X: X_train, Y: y_train}))
              mse_test.append(net.run(mse, feed_dict={X: X_test, Y: y_test}))
              print('MSE Train: ', mse_train[-1])
              print('MSE Test: ', mse_test[-1])
              # Prediction
              pred = net.run(out, feed_dict={X: X_test})
              line2.set_ydata(pred)
              plt.title('Epoch ' + str(e) + ', Batch ' + str(i))
              plt.pause(0.01)
  