fit_min_max_scaler.py

import glob
from sklearn.preprocessing import MinMaxScaler

training_files = glob.glob(os.path.join(train_dir, '*'))
validation_files = glob.glob(os.path.join(validation_dir, '*'))

# Declare constants like BATCH_SIZE and NO_OF_EPOCHS
BATCH_SIZE = 32
NO_OF_EPOCHS = 3

# lets construct one big numpy array from all the train files
X_train = np.empty((1, NO_OF_FEATURES))
for train_file in training_files:
    file_np = np.load(train_file)
    X_train = np.vstack((X_train, file_np))

# This is done leave the first row which is empty and of no use.
X_train = X_train[1:, :]

# lets construct one big numpy array from all validation files
X_validation = np.empty((1, NO_OF_FEATURES))
for validation_file in validation_files:
    file_np = np.load(validation_file)
    X_validation = np.vstack((X_validation, file_np))

# This is done leave the first row which is empty and of no use.
X_validation = X_validation[1:, :]

# Time for fitting MinMax scaler to our data. 

# We are training autoencoder so we only need inputs (X) not labels (y)
min_max_scaler = MinMaxScaler()
min_max_scaler.fit(X_train)
# We will fit the scaler on training data. 

# We will scale the training data and validation data after fitting.
X_train_scaled = min_max_scaler.transform(X_train)
X_validation_scaled = min_max_scaler.transform(X_validation)