mu = 0 sigma = 0.1 weights = { 'wc1': tf.Variable(tf.truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma)), #truncated_normal(shape=(5, 5, 1, 6), mean = mu, stddev = sigma) 'wc2': tf.Variable(tf.truncated_normal(shape=(5, 5, 6, 16), mean = mu, stddev = sigma)), 'wf1': tf.Variable(tf.truncated_normal(shape=(400, 120), mean = mu, stddev = sigma)), 'wf2': tf.Variable(tf.truncated_normal(shape=(120, 84), mean = mu, stddev = sigma)), 'out': tf.Variable(tf.truncated_normal(shape=(84, 10), mean = mu, stddev = sigma))} biases = { 'bc1': tf.Variable(tf.zeros([6])), 'bc2': tf.Variable(tf.zeros([16])), 'bf1': tf.Variable(tf.zeros([120])), 'bf2': tf.Variable(tf.zeros([84])), 'out': tf.Variable(tf.zeros([10]))} #Layer 1: Convolutional. Input = 32x32x1. Output = 28x28x6. out1 = tf.nn.conv2d(x, weights['wc1'], strides=[1, 1, 1, 1], padding='VALID') out1 = tf.nn.bias_add(out1,biases['bc1']) #Activation. out1 = tf.nn.relu(out1) #Pooling. Input = 28x28x6. Output = 14x14x6. out1 = tf.nn.max_pool( out1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') #Layer 2: Convolutional. Output = 10x10x16. out2 = tf.nn.conv2d(out1, weights['wc2'], strides=[1, 1, 1, 1], padding='VALID') out2 = tf.nn.bias_add(out2,biases['bc2']) #Activation. out2 = tf.nn.relu(out2) #Pooling. Input = 10x10x16. Output = 5x5x16. out2 = tf.nn.max_pool( out2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='VALID') #Flatten. Input = 5x5x16. Output = 400. flat = flatten(out2) #Layer 3: Fully Connected. Input = 400. Output = 120. out3 = tf.matmul(flat,weights['wf1']) out3 = tf.nn.bias_add(out3,biases['bf1']) #Activation. out3 = tf.nn.relu(out3) #Layer 4: Fully Connected. Input = 120. Output = 84. out4 = tf.matmul(out3,weights['wf2']) out4 = tf.nn.bias_add(out4,biases['bf2']) #Activation. out4 = tf.nn.relu(out4) #Layer 5: Fully Connected. Input = 84. Output = 10. logits = tf.matmul(out4,weights['out']) logits = tf.nn.bias_add(logits,biases['out']) return logits
月度归档:2021年04月
Pickle模块的用法
pickle.dump( data, open( "save.p", "wb" ) )
data= pickle.load( open( "save.p", "rb" ) )
用Python处理矩阵运算
numpy格式的矩阵乘以一个标量则每个元素都乘以这个标量。
numpy格式的矩阵加上一个标量则每个元素都加上这个标量。