我正在TensorFlow训练我的深层网络，我正试图用它来学习学习率衰减。据我所知，我应该使用train.exponential_decay函数 - 它将使用各种参数计算当前训练步骤的正确学习速率值。我只需要提供一个现在执行的步骤。我怀疑我应该像往常一样使用tf.placeholder（tf.int32），当我需要向网络提供一些东西时，但似乎我错了。当我这样做时，我得到以下错误：
TypeError: Input 'ref' of 'AssignAdd' Op requires l-value input
我做错了什么？不幸的是，我还没有设法找到一些有关衰变的网络训练的好例子。我的整个代码如下。网络有2个隐藏的ReLU层，对权重有L2惩罚，并且在两个隐藏层都有丢失。
#We try the following - 2 ReLU layers
#Dropout on both of them
#Also L2 regularization on them
#and learning rate decay also
#batch size for SGD
batch_size = 128
#beta parameter for L2 loss
beta = 0.001
#that's how many hidden neurons we want
num_hidden_neurons = 1024
#learning rate decay
#starting value, number of steps decay is performed,
#size of the decay
start_learning_rate = 0.05
decay_steps = 1000
decay_size = 0.95
#building tensorflow graph
graph = tf.Graph()
with graph.as_default():
# Input data. For the training data, we use a placeholder that will be fed
# at run time with a training minibatch.
tf_train_dataset = tf.placeholder(tf.float32,
shape=(batch_size, image_size * image_size))
tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels))
tf_valid_dataset = tf.constant(valid_dataset)
tf_test_dataset = tf.constant(test_dataset)
#now let's build our first hidden layer
#its weights
hidden_weights_1 = tf.Variable(
tf.truncated_normal([image_size * image_size, num_hidden_neurons]))
hidden_biases_1 = tf.Variable(tf.zeros([num_hidden_neurons]))
#now the layer 1 itself. It multiplies data by weights, adds biases
#and takes ReLU over result
hidden_layer_1 = tf.nn.relu(tf.matmul(tf_train_dataset, hidden_weights_1) + hidden_biases_1)
#add dropout on hidden layer 1
#we pick up the probabylity of switching off the activation
#and perform the switch off of the activations
keep_prob = tf.placeholder("float")
hidden_layer_drop_1 = tf.nn.dropout(hidden_layer_1, keep_prob)
#now let's build our second hidden layer
#its weights
hidden_weights_2 = tf.Variable(
tf.truncated_normal([num_hidden_neurons, num_hidden_neurons]))
hidden_biases_2 = tf.Variable(tf.zeros([num_hidden_neurons]))
#now the layer 2 itself. It multiplies data by weights, adds biases
#and takes ReLU over result
hidden_layer_2 = tf.nn.relu(tf.matmul(hidden_layer_drop_1, hidden_weights_2) + hidden_biases_2)
#add dropout on hidden layer 2
#we pick up the probabylity of switching off the activation
#and perform the switch off of the activations
hidden_layer_drop_2 = tf.nn.dropout(hidden_layer_2, keep_prob)
#time to go for output linear layer
#out weights connect hidden neurons to output labels
#biases are added to output labels
out_weights = tf.Variable(
tf.truncated_normal([num_hidden_neurons, num_labels]))
out_biases = tf.Variable(tf.zeros([num_labels]))
#compute output
#notice that upon training we use the switched off activations
#i.e. the variaction of hidden_layer with the dropout active
out_layer = tf.matmul(hidden_layer_drop_2,out_weights) + out_biases
#our real output is a softmax of prior result
#and we also compute its cross-entropy to get our loss
#Notice - we introduce our L2 here
loss = (tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(
out_layer, tf_train_labels) +
beta*tf.nn.l2_loss(hidden_weights_1) +
beta*tf.nn.l2_loss(hidden_biases_1) +
beta*tf.nn.l2_loss(hidden_weights_2) +
beta*tf.nn.l2_loss(hidden_biases_2) +
beta*tf.nn.l2_loss(out_weights) +
beta*tf.nn.l2_loss(out_biases)))
#variable to count number of steps taken
global_step = tf.placeholder(tf.int32)
#compute current learning rate
learning_rate = tf.train.exponential_decay(start_learning_rate, global_step, decay_steps, decay_size)
#use it in optimizer
optimizer = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss, global_step=global_step)
#nice, now let's calculate the predictions on each dataset for evaluating the
#performance so far
# Predictions for the training, validation, and test data.
train_prediction = tf.nn.softmax(out_layer)
valid_relu_1 = tf.nn.relu( tf.matmul(tf_valid_dataset, hidden_weights_1) + hidden_biases_1)
valid_relu_2 = tf.nn.relu( tf.matmul(valid_relu_1, hidden_weights_2) + hidden_biases_2)
valid_prediction = tf.nn.softmax( tf.matmul(valid_relu_2, out_weights) + out_biases)
test_relu_1 = tf.nn.relu( tf.matmul( tf_test_dataset, hidden_weights_1) + hidden_biases_1)
test_relu_2 = tf.nn.relu( tf.matmul( test_relu_1, hidden_weights_2) + hidden_biases_2)
test_prediction = tf.nn.softmax(tf.matmul(test_relu_2, out_weights) + out_biases)
#now is the actual training on the ANN we built
#we will run it for some number of steps and evaluate the progress after
#every 500 steps
#number of steps we will train our ANN
num_steps = 3001
#actual training
with tf.Session(graph=graph) as session:
tf.initialize_all_variables().run()
print("Initialized")
for step in range(num_steps):
# Pick an offset within the training data, which has been randomized.
# Note: we could use better randomization across epochs.
offset = (step * batch_size) % (train_labels.shape[0] - batch_size)
# Generate a minibatch.
batch_data = train_dataset[offset:(offset + batch_size), :]
batch_labels = train_labels[offset:(offset + batch_size), :]
# Prepare a dictionary telling the session where to feed the minibatch.
# The key of the dictionary is the placeholder node of the graph to be fed,
# and the value is the numpy array to feed to it.
feed_dict = {tf_train_dataset : batch_data, tf_train_labels : batch_labels, keep_prob : 0.5, global_step: step}
_, l, predictions = session.run(
[optimizer, loss, train_prediction], feed_dict=feed_dict)
if (step % 500 == 0):
print("Minibatch loss at step %d: %f" % (step, l))
print("Minibatch accuracy: %.1f%%" % accuracy(predictions, batch_labels))
print("Validation accuracy: %.1f%%" % accuracy(
valid_prediction.eval(), valid_labels))
print("Test accuracy: %.1f%%" % accuracy(test_prediction.eval(), test_labels))
答案 0 :(得分：1)
请尝试使用global_step。
Variable的占位符
global_step = tf.Variable(0)
您必须从global_step中删除feed_dict。请注意，您不必手动递增global_step，tensorflow会自动为您执行此操作。