import math

import numpy as np

import h5py

import matplotlib.pyplot as plt

import tensorflow as tf

from tensorflow.python.framework import ops

from sklearn import preprocessing

from sklearn.preprocessing import OneHotEncoder

from tf_utils import load_dataset, convert_to_one_hot

from backwardPropagation import model

from keras.utils import to_categorical

X_train, X_test, y_train, y_test = load_dataset()

# Take transpose of the input data and also normalize it

X_train = X_train.T

X_train = (X_train - X_train.mean()) / (X_train.max() - X_train.min())

X_train = X_train.fillna(0)

X_test = X_test.T

X_test = (X_test - X_test.mean()) / (X_test.max() - X_test.min())

X_test = X_test.fillna(0)

# Convert training and test labels to one hot matrices

y_train = to_categorical(y_train,9)

y_train = y_train.T

#print(y_train)

#print(y_train.shape)

y_test = to_categorical(y_test,9)

y_test = y_test.T

#print(X_train)

#print(y_train)

#print(X_test)

#print(y_test)

parameters = model(X_train,y_train,X_test,y_test)

print(parameters["W1"])