import numpy as np
from sklearn.neural_network import MLPClassifier
path = 'mnist.npz'
f = np.load(path)
X_train , y_train = f['x_train'], f['y_train']
X_test , y_test = f['x_test'], f['y_test']
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train /= 255.
X_test /= 255.
X_train = X_train.reshape(60000,784)
X_test = X_test.reshape(10000,784)
mlp = MLPClassifier(hidden_layer_sizes=(100,100,100))
mlp.fit(X_train,y_train)
print('Training set score: %f' % mlp.score(X_train,y_train))
print('Test set score: %f' % mlp.score(X_test,y_test))
# print(X_train[0])
'''
print(X_train.shape)
print(y_train.shape)
print(X_test.shape)
print(y_test.shape)
'''
