def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
    self.params = {}
    self.params['W1'] = (weight_init_std * np.random.randn(input_size, hidden_size))
    self.params['b1'] = np.zeros(hidden_size)
    self.params['W2'] = (weight_init_std * np.random.randn(hidden_size, output_size))
    self.params['b2'] = np.zeros(output_size)
    self.layers = OrderedDict()
    self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1'])
    self.layers['Relu1'] = Relu()
    self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2'])
    self.lastLayer = SoftmaxWithLoss()
def __init__(self, input_size, hidden_size, output_size, weight_init_std=0.01):
    self.params = {}
    self.params['W1'] = (weight_init_std * np.random.randn(input_size, hidden_size))
    self.params['b1'] = np.zeros(hidden_size)
    self.params['W2'] = (weight_init_std * np.random.randn(hidden_size, output_size))
    self.params['b2'] = np.zeros(output_size)
    self.layers = OrderedDict()
    self.layers['Affine1'] = Affine(self.params['W1'], self.params['b1'])
    self.layers['Relu1'] = Relu()
    self.layers['Affine2'] = Affine(self.params['W2'], self.params['b2'])
    self.lastLayer = SoftmaxWithLoss()