基于tensorflow实现猫狗识别代码(CNN)

# ========================================================================= import tensorflow as tf # ========================================================================= # # images image batch 4D tensor tf.float32 [batch_size, width, height, channels] # logits float [batch_size n_classes] def inference(images, batch_size, n_classes): # # conv1 # with tf.variable_scope('conv1') as scope: weights = tf.Variable(tf.truncated_normal(shape=[3, 3, 3, 64], stddev=1.0, dtype=tf.float32), name='weights', dtype=tf.float32) biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[64]), name='biases', dtype=tf.float32) conv = tf.nn.conv2d(images, weights, strides=[1, 1, 1, 1], padding='SAME') pre_activation = tf.nn.bias_add(conv, biases) conv1 = tf.nn.relu(pre_activation, name=scope.name) # pooling1 # with tf.variable_scope('pooling1_lrn') as scope: pool1 = tf.nn.max_pool(conv1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name='pooling1') norm1 = tf.nn.lrn(pool1, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm1') # conv2 with tf.variable_scope('conv2') as scope: weights = tf.Variable(tf.truncated_normal(shape=[3, 3, 64, 16], stddev=0.1, dtype=tf.float32), name='weights', dtype=tf.float32) biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[16]), name='biases', dtype=tf.float32) conv = tf.nn.conv2d(norm1, weights, strides=[1, 1, 1, 1], padding='SAME') pre_activation = tf.nn.bias_add(conv, biases) conv2 = tf.nn.relu(pre_activation, name='conv2') # pooling2 with tf.variable_scope('pooling2_lrn') as scope: norm2 = tf.nn.lrn(conv2, depth_radius=4, bias=1.0, alpha=0.001 / 9.0, beta=0.75, name='norm2') pool2 = tf.nn.max_pool(norm2, ksize=[1, 3, 3, 1], strides=[1, 1, 1, 1], padding='SAME', name='pooling2') # fc3 with tf.variable_scope('local3') as scope: reshape = tf.reshape(pool2, shape=[batch_size, -1]) dim = reshape.get_shape()[1].value weights = tf.Variable(tf.truncated_normal(shape=[dim, 128], stddev=0.005, dtype=tf.float32), name='weights', dtype=tf.float32) biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[128]), name='biases', dtype=tf.float32) local3 = tf.nn.relu(tf.matmul(reshape, weights) + biases, name=scope.name) # fc4 with tf.variable_scope('local4') as scope: weights = tf.Variable(tf.truncated_normal(shape=[128, 128], stddev=0.005, dtype=tf.float32), name='weights', dtype=tf.float32) biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[128]), name='biases', dtype=tf.float32) local4 = tf.nn.relu(tf.matmul(local3, weights) + biases, name='local4') # dropout # with tf.variable_scope('dropout') as scope: # drop_out = tf.nn.dropout(local4, 0.8) with tf.variable_scope('softmax_linear') as scope: weights = tf.Variable(tf.truncated_normal(shape=[128, n_classes], stddev=0.005, dtype=tf.float32), name='softmax_linear', dtype=tf.float32) biases = tf.Variable(tf.constant(value=0.1, dtype=tf.float32, shape=[n_classes]), name='biases', dtype=tf.float32) softmax_linear = tf.add(tf.matmul(local4, weights), biases, name='softmax_linear') return softmax_linear # ----------------------------------------------------------------------------- # cal loss def losses(logits, labels): with tf.variable_scope('loss') as scope: cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels, name='xentropy_per_example') loss = tf.reduce_mean(cross_entropy, name='loss') tf.summary.scalar(scope.name + '/loss', loss) return loss # -------------------------------------------------------------------------- # loss # loss learning_rate # train_op def trainning(loss, learning_rate): with tf.name_scope('optimizer'): optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate) global_step = tf.Variable(0, name='global_step', trainable=False) train_op = optimizer.minimize(loss, global_step=global_step) return train_op # ----------------------------------------------------------------------- def evaluation(logits, labels): with tf.variable_scope('accuracy') as scope: correct = tf.nn.in_top_k(logits, labels, 1) correct = tf.cast(correct, tf.float16) accuracy = tf.reduce_mean(correct) tf.summary.scalar(scope.name + '/accuracy', accuracy) return accuracy