from __future__ import print_function

import numpy as np
import matplotlib.pyplot as plt


class TwoLayerNet(object):
    """
    A two-layer fully-connected neural network. The net has an input dimension
    of N, a hidden layer dimension of H, and performs classification over C 
    classes.
    We train the network with a softmax loss function and L2 regularization on
    the weight matrices. The network uses a ReLU nonlinearity after the first
    fully connected layer.
    In other words, the network has the following architecture:
    input - fully connected layer - ReLU - fully connected layer - softmax
    The outputs of the second fully-connected layer are the scores for each
    class.
    """

    def __init__(self, input_size, hidden_size, output_size, std=1e-4):
        """
        Initialize the model. Weights are initialized to small random values
        and biases are initialized to zero. Weights and biases are stored in
        the variable self.params, which is a dictionary with the following keys
        W1: First layer weights; has shape (D, H)
        b1: First layer biases; has shape (H,)
        W2: Second layer weights; has shape (H, C)
        b2: Second layer biases; has shape (C,)
        Inputs:
        - input_size: The dimension D of the input data.
        - hidden_size: The number of neurons H in the hidden layer.
        - output_size: The number of classes C.
        """
        self.params = {}
        self.params['W1'] = std * np.random.randn(input_size, hidden_size)
        self.params['b1'] = np.zeros(hidden_size)
        self.params['W2'] = std * 
np.random.randn(hidden_size, output_size)