我实现了一个AlexNet模型。我想提取训练模型在全连接分类层之前的特征向量
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首先，我想训练模型（下面包括了训练和测试的评估方法）。
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如何获取训练/测试集中所有图像在分类之前的最终输出特征向量列表（在前向传递过程中）？
这是代码（完整版本可在 https://github.com/aymericdamien/TensorFlow-Examples/blob/master/examples/3%20-%20Neural%20Networks/alexnet.py 查看）：
weights = { 'wc1': tf.Variable(tf.random_normal([3, 3, 1, 64])), 'wc2': tf.Variable(tf.random_normal([3, 3, 64, 128])), 'wc3': tf.Variable(tf.random_normal([3, 3, 128, 256])), 'wd1': tf.Variable(tf.random_normal([4*4*256, 1024])), 'wd2': tf.Variable(tf.random_normal([1024, 1024])), 'out': tf.Variable(tf.random_normal([1024, 10]))}biases = { 'bc1': tf.Variable(tf.random_normal([64])), 'bc2': tf.Variable(tf.random_normal([128])), 'bc3': tf.Variable(tf.random_normal([256])), 'bd1': tf.Variable(tf.random_normal([1024])), 'bd2': tf.Variable(tf.random_normal([1024])), 'out': tf.Variable(tf.random_normal([n_classes]))}def alex_net(_X, _weights, _biases, _dropout): # Reshape input picture _X = tf.reshape(_X, shape=[-1, 28, 28, 1]) # Convolution Layer conv1 = conv2d('conv1', _X, _weights['wc1'], _biases['bc1']) # Max Pooling (down-sampling) pool1 = max_pool('pool1', conv1, k=2) # Apply Normalization norm1 = norm('norm1', pool1, lsize=4) # Apply Dropout norm1 = tf.nn.dropout(norm1, _dropout) # Convolution Layer conv2 = conv2d('conv2', norm1, _weights['wc2'], _biases['bc2']) ... # right before feeding the fully connected, classification layers # I'm interested in the vector after the weights # are applied during the forward pass of a trained model. dense1 = tf.reshape(norm3, [-1, _weights['wd1'].get_shape().as_list()[0]]) # Relu activation dense1 = tf.nn.relu(tf.matmul(dense1, _weights['wd1']) + _biases['bd1'], name='fc1') # Relu activation dense2 = tf.nn.relu(tf.matmul(dense1, _weights['wd2']) + _biases['bd2'], name='fc2') # Output, class prediction out = tf.matmul(dense2, _weights['out']) + _biases['out'] return outpred = alex_net(x, weights, biases, keep_prob)cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(pred, y))optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# Evaluate modelcorrect_pred = tf.equal(tf.argmax(pred,1), tf.argmax(y,1))accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))# Launch the graphwith tf.Session() as sess: sess.run(init) step = 1 # Keep training until reach max iterations summary_writer = tf.train.SummaryWriter('/tmp/tensorflow_logs', graph_def=sess.graph_def) while step * batch_size < training_iters: batch_xs, batch_ys = mnist.train.next_batch(batch_size) # Fit training using batch data sess.run(optimizer, feed_dict={x: batch_xs, y: batch_ys, keep_prob: dropout}) if step % display_step == 0: # Calculate batch accuracy acc = sess.run(accuracy, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) # Calculate batch loss loss = sess.run(cost, feed_dict={x: batch_xs, y: batch_ys, keep_prob: 1.}) print "Iter " + str(step*batch_size) + ", Minibatch Loss= " \ + "{:.6f}".format(loss) + ", Training Accuracy= " + "{:.5f}".format(acc) step += 1 print "Optimization Finished!" # Calculate accuracy for 256 mnist test images print "Testing Accuracy:", sess.run(accuracy, feed_dict={x: mnist.test.images[:256], y: mnist.test.labels[:256], keep_prob: 1.})回答：
听起来你想要的是alex_net()中的dense2的值。如果是这样，你需要除了out之外还返回dense2，所以
return out变为
return dense2, out并且
pred = alex_net(x, weights, biases, keep_prob)变为
before_classification_layer, pred = alex_net(...)然后你可以在调用sess.run()时获取before_classification_layer的值。请参阅tf.Session.run在https://www.tensorflow.org/versions/0.6.0/api_docs/python/client.html#Session.run。请注意，fetches可以是一个列表，所以为了避免在你的示例代码中两次评估你的图，你可以这样做
# Calculate batch accuracy and lossacc, loss = sess.run([accuracy, cost], feed_dict={...})而不是
# Calculate batch accuracyacc = sess.run(accuracy, feed_dict={...})# Calculate batch lossloss = sess.run(cost, feed_dict={...})（在需要时将before_classification_layer添加到该列表中。）
