--- a
+++ b/ML Training.py
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+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"])
+
+
+
+
+
+
+
+
+