如何使用TF函数fully_connected为完全连接的层添加退出？

Question

中提到的tensorflow功能fully_connected没有参数添加退出最后一层。有办法吗？

Answer 1

请查看专家的Deep MNIST教程和mnist_deep.py的源代码

# Fully connected layer 1 -- after 2 round of downsampling, our 28x28 image
# is down to 7x7x64 feature maps -- maps this to 1024 features.
with tf.name_scope('fc1'):
    W_fc1 = weight_variable([7 * 7 * 64, 1024])
    b_fc1 = bias_variable([1024])

    h_pool2_flat = tf.reshape(h_pool2, [-1, 7*7*64])
    h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)

# Dropout - controls the complexity of the model, prevents co-adaptation of
# features.
with tf.name_scope('dropout'):
    keep_prob = tf.placeholder(tf.float32)
    h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)

# Map the 1024 features to 10 classes, one for each digit
with tf.name_scope('fc2'):
    W_fc2 = weight_variable([1024, 10])
    b_fc2 = bias_variable([10])

    y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2

或者，如果您想使用tf.contrib.layers.fully_connected，可以执行以下操作：

h_pool2_flatten = tf.contrib.layers.flatten.flatten(h_pool2)
h_fc1 = tf.contrib.layers.fully_connected(h_pool2_flatten, 1024, scope='fc1')
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.contrib.layers.dropout(h_fc1, keep_prob)
y_conv = tf.contrib.layers.fully_connected(h_fc1_drop, 10, activation_fn=None, scope='fc2')