-2
fully_connected中提到的張量流函數沒有參數爲最後一層添加dropout。有沒有辦法?如何爲TF完全連接的完全連接圖層添加dropout?
fully_connected中提到的張量流函數沒有參數爲最後一層添加dropout。有沒有辦法?如何爲TF完全連接的完全連接圖層添加dropout?
我這樣做:
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1)
keep_prob = tf.placeholder(tf.float32)
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob)
請看教程Deep MNIST for Experts和mnist_deep.py
# 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
源代碼,或者,如果你想使用tf.contrib.layers.fully_connected
你可以做這樣的事情:
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')
你能否詳細解釋你的問題,你想做什麼?通常最後一層是預測某個類或值的那個層,你想在那裏使用drop_out來實現什麼。 –
@VivekKumar人已經正確回答了。爲什麼仍然是-2? –