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220 rindas
9.1 KiB
220 rindas
9.1 KiB
from __future__ import print_function
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from builtins import range
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from builtins import object
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import numpy as np
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import matplotlib.pyplot as plt
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from past.builtins import xrange
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class TwoLayerNet(object):
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"""
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A two-layer fully-connected neural network. The net has an input dimension of
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N, a hidden layer dimension of H, and performs classification over C classes.
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We train the network with a softmax loss function and L2 regularization on the
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weight matrices. The network uses a ReLU nonlinearity after the first fully
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connected layer.
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In other words, the network has the following architecture:
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input - fully connected layer - ReLU - fully connected layer - softmax
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The outputs of the second fully-connected layer are the scores for each class.
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"""
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def __init__(self, input_size, hidden_size, output_size, std=1e-4):
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"""
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Initialize the model. Weights are initialized to small random values and
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biases are initialized to zero. Weights and biases are stored in the
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variable self.params, which is a dictionary with the following keys:
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W1: First layer weights; has shape (D, H)
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b1: First layer biases; has shape (H,)
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W2: Second layer weights; has shape (H, C)
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b2: Second layer biases; has shape (C,)
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Inputs:
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- input_size: The dimension D of the input data.
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- hidden_size: The number of neurons H in the hidden layer.
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- output_size: The number of classes C.
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"""
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self.params = {}
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self.params['W1'] = std * np.random.randn(input_size, hidden_size)
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self.params['b1'] = np.zeros(hidden_size)
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self.params['W2'] = std * np.random.randn(hidden_size, output_size)
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self.params['b2'] = np.zeros(output_size)
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def loss(self, X, y=None, reg=0.0):
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"""
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Compute the loss and gradients for a two layer fully connected neural
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network.
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Inputs:
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- X: Input data of shape (N, D). Each X[i] is a training sample.
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- y: Vector of training labels. y[i] is the label for X[i], and each y[i] is
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an integer in the range 0 <= y[i] < C. This parameter is optional; if it
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is not passed then we only return scores, and if it is passed then we
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instead return the loss and gradients.
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- reg: Regularization strength.
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Returns:
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If y is None, return a matrix scores of shape (N, C) where scores[i, c] is
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the score for class c on input X[i].
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If y is not None, instead return a tuple of:
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- loss: Loss (data loss and regularization loss) for this batch of training
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samples.
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- grads: Dictionary mapping parameter names to gradients of those parameters
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with respect to the loss function; has the same keys as self.params.
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"""
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# Unpack variables from the params dictionary
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W1, b1 = self.params['W1'], self.params['b1']
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W2, b2 = self.params['W2'], self.params['b2']
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N, D = X.shape
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# Compute the forward pass
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scores = None
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#############################################################################
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# TODO: 执行向前传播，计算输入数据的每个类的score。
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# 将结果存储在scores变量中，该变量应该是一个(N, C)维的数组。
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#############################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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# If the targets are not given then jump out, we're done
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if y is None:
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return scores
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# Compute the loss
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loss = None
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#############################################################################
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# TODO: 完成向前传播，计算损失。
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# 这应该包括数据损失和W1和W2的L2正则化项。
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# 将结果存储在变量loss中，它应该是一个标量。
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# 使用Softmax损失函数。
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#############################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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# Backward pass: compute gradients
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grads = {}
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#############################################################################
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# TODO: 计算反向传播，计算权重和偏置值的梯度, 将结果存储在grads字典中。
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# 例如，grads['W1']存储W1的梯度，并且和W1是相同大小的矩阵。
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#############################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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return loss, grads
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def train(self, X, y, X_val, y_val,
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learning_rate=1e-3, learning_rate_decay=0.95,
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reg=5e-6, num_iters=100,
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batch_size=200, verbose=False):
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"""
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Train this neural network using stochastic gradient descent.
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Inputs:
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- X: A numpy array of shape (N, D) giving training data.
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- y: A numpy array f shape (N,) giving training labels; y[i] = c means that
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X[i] has label c, where 0 <= c < C.
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- X_val: A numpy array of shape (N_val, D) giving validation data.
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- y_val: A numpy array of shape (N_val,) giving validation labels.
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- learning_rate: Scalar giving learning rate for optimization.
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- learning_rate_decay: Scalar giving factor used to decay the learning rate
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after each epoch.
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- reg: Scalar giving regularization strength.
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- num_iters: Number of steps to take when optimizing.
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- batch_size: Number of training examples to use per step.
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- verbose: boolean; if true print progress during optimization.
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"""
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num_train = X.shape[0]
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iterations_per_epoch = max(num_train / batch_size, 1)
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# Use SGD to optimize the parameters in self.model
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loss_history = []
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train_acc_history = []
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val_acc_history = []
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for it in range(num_iters):
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X_batch = None
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y_batch = None
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#########################################################################
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# TODO: 创建一个随机的数据和标签的mini-batch，存储在X_batch和y_batch中。
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#########################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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# Compute loss and gradients using the current minibatch
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loss, grads = self.loss(X_batch, y=y_batch, reg=reg)
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loss_history.append(loss)
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#########################################################################
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# TODO: 使用grads字典中的梯度来更新网络参数(参数存储在字典self.params中)
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# 使用随机梯度下降法。
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#########################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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if verbose and it % 100 == 0:
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print('iteration %d / %d: loss %f' % (it, num_iters, loss))
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# Every epoch, check train and val accuracy and decay learning rate.
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if it % iterations_per_epoch == 0:
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# Check accuracy
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train_acc = (self.predict(X_batch) == y_batch).mean()
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val_acc = (self.predict(X_val) == y_val).mean()
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train_acc_history.append(train_acc)
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val_acc_history.append(val_acc)
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# Decay learning rate
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learning_rate *= learning_rate_decay
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return {
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'loss_history': loss_history,
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'train_acc_history': train_acc_history,
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'val_acc_history': val_acc_history,
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}
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def predict(self, X):
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"""
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Use the trained weights of this two-layer network to predict labels for
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data points. For each data point we predict scores for each of the C
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classes, and assign each data point to the class with the highest score.
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Inputs:
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- X: A numpy array of shape (N, D) giving N D-dimensional data points to
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classify.
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Returns:
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- y_pred: A numpy array of shape (N,) giving predicted labels for each of
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the elements of X. For all i, y_pred[i] = c means that X[i] is predicted
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to have class c, where 0 <= c < C.
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"""
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y_pred = None
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###########################################################################
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# TODO: Implement this function; it should be VERY simple! #
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###########################################################################
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# *****START OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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pass
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# *****END OF YOUR CODE (DO NOT DELETE/MODIFY THIS LINE)*****
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return y_pred
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