115 lines
5.5 KiB
115 lines
5.5 KiB
from builtins import object
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import numpy as np
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from daseCV.layers import *
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from daseCV.fast_layers import *
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from daseCV.layer_utils import *
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class ThreeLayerConvNet(object):
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"""
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A three-layer convolutional network with the following architecture:
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conv - relu - 2x2 max pool - affine - relu - affine - softmax
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The network operates on minibatches of data that have shape (N, C, H, W)
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consisting of N images, each with height H and width W and with C input
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channels.
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"""
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def __init__(self, input_dim=(3, 32, 32), num_filters=32, filter_size=7,
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hidden_dim=100, num_classes=10, weight_scale=1e-3, reg=0.0,
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dtype=np.float32):
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"""
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Initialize a new network.
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Inputs:
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- input_dim: Tuple (C, H, W) giving size of input data
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- num_filters: Number of filters to use in the convolutional layer
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- filter_size: Width/height of filters to use in the convolutional layer
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- hidden_dim: Number of units to use in the fully-connected hidden layer
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- num_classes: Number of scores to produce from the final affine layer.
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- weight_scale: Scalar giving standard deviation for random initialization
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of weights.
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- reg: Scalar giving L2 regularization strength
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- dtype: numpy datatype to use for computation.
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"""
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self.params = {}
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self.reg = reg
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self.dtype = dtype
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############################################################################
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# TODO: 初始化三层卷积网络的权重和偏差。
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# 权重应用以0.0为中心的高斯初始化，标准差等于weight_scale；
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# 偏差应初始化为零。所有权重和偏差应存储在字典self.params中。
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# 使用键“ W1”和“ b1”存储卷积层的权重和偏差；使用键“ W2”和“ b2”
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# 表示隐藏仿射层的权重和偏差，并使用键“ W3”和“ b3”表示输出仿射层的
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# 权重和偏差。重要说明：对于本次作业，您可以假设第一个卷积层的padding和
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# stride以及设置了，这样**输入的width和height就保留了**。看一下loss()
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# 函数的前部分它是如何做的。
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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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############################################################################
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# END OF YOUR CODE #
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############################################################################
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for k, v in self.params.items():
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self.params[k] = v.astype(dtype)
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def loss(self, X, y=None):
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"""
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Evaluate loss and gradient for the three-layer convolutional network.
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Input / output: Same API as TwoLayerNet in fc_net.py.
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"""
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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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W3, b3 = self.params['W3'], self.params['b3']
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# pass conv_param to the forward pass for the convolutional layer
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# Padding and stride chosen to preserve the input spatial size
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filter_size = W1.shape[2]
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conv_param = {'stride': 1, 'pad': (filter_size - 1) // 2}
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# pass pool_param to the forward pass for the max-pooling layer
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pool_param = {'pool_height': 2, 'pool_width': 2, 'stride': 2}
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scores = None
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############################################################################
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# TODO: 实现三层卷积网络的正向传播，计算X的每类的分数并将其存储在scores变量中。
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#请注意，您可以在实现中使用daseCV/fast_layers.py和daseCV/layer_utils.py中定义的功能（已导入）。
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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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############################################################################
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# END OF YOUR CODE #
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############################################################################
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if y is None:
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return scores
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loss, grads = 0, {}
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############################################################################
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# TODO: 完成三层卷积网络的反向传播，将损失和梯度存储在变量loss和grads中。
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# 使用softmax计算损失，并使用grads[k]保存self.params[k]的梯度。不要忘记增加L2正则化！
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#
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# NOTE: 为确保您的实现与我们的实现相同并通过自动化测试，请确保您的L2正则化系数为0.5，
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# 以简化梯度的表达式。
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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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############################################################################
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# END OF YOUR CODE #
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############################################################################
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return loss, grads
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