我正在使用 notMNIST 数据集训练一个神经网络来识别字符,但是一旦运行它,它的准确性在每次迭代后都会保持相对恒定。
我尝试过降低学习率,但没有什么不同。可能是什么问题?
我认为问题可能出在 tf.nn.relu() 方法的实现上,以及我如何计算预测,因为我对 tensorflow 和神经网络还很陌生
这是我的程序运行的截图,可以看到训练集、验证集、测试集的准确率都很差
num_steps=801
def accuracy(predictions, labels):
return (100.0 * np.sum(np.argmax(predictions,1) == np.argmax(labels,1))
/ predictions.shape[0])
with tf.Session(graph=graph) as session:
#this is a one-time operation which ensure the parameters get initialized
#we described in the graph: random weights for the matrix, zeros for the
#biases.
tf.global_variables_initializer().run()
print("initialized")
for step in range(num_steps):
#run the computations. we tell .run() that we want to run the optimizer,
#and get the loss value and the training predictions returned as numpy
#arrays.
_, l, predictions = session.run([optimizer,loss, train_prediction])
if (step % 100 ==0):
print("loss at step %d: %f" % (step,l))
print("Training accuracy: %.1f%%" % accuracy(
predictions, train_labels[:train_subset,:]))
#calling .eval() on valid_prediction is basically like calling run(), but
#just to get that one numpy array. Note that it recomputes all its graph
#dependencies.
print("Validation accuracy: %.1f%%" % accuracy(
valid_prediction.eval(), valid_labels))
print("test accuracy: %.1f%%" % accuracy(test_prediction.eval(),test_labels))
batch_size = 128
hidden_nodes = 1024
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), name="td")
tf_train_labels = tf.placeholder(tf.float32, shape=(batch_size, num_labels), name="tl")
tf_valid_dataset = tf.constant(valid_dataset)
tf_test_dataset = tf.constant(test_dataset)
#variables
weights1 = tf.Variable(
tf.truncated_normal([image_size*image_size, hidden_nodes]))
biases1 = tf.Variable(tf.zeros([hidden_nodes]))
weights2 =tf.Variable(
tf.truncated_normal([hidden_nodes, num_labels]))
biases2 = tf.Variable(tf.zeros([num_labels]))
#training computation.
relu1 = tf.nn.relu(tf.matmul(tf_train_dataset, weights1) + biases1)
relu_out= tf.nn.relu(tf.matmul(relu1, weights2) + biases2)
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=relu_out,labels=tf_train_labels))
#optimizer
optimizer = tf.train.GradientDescentOptimizer(0.25).minimize(loss)
#predictions for the training, validation, and test data
train_prediction = relu_out
valid_prediction = tf.nn.relu(tf.matmul(tf.nn.relu(tf.matmul(tf_valid_dataset, weights1) + biases1), weights2) + biases2)
test_prediction = tf.nn.relu(tf.matmul(tf.nn.relu(tf.matmul(tf_test_dataset, weights1) + biases1), weights2) + biases2)
num_steps = 3001
with tf.Session(graph=graph) as session:
tf.global_variables_initializer().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}
_, 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))
最佳答案
正如我所想,问题出在 relu() 方法的实现上。
在计算部分,我使用了 relu() 两次,而我应该只使用一次。更改后,它最终看起来像这样。
logits_1 = tf.matmul(tf_train_dataset, weights1) + biases1
relu1 = tf.nn.relu(logits_1)
logits_2 = tf.matmul(relu1, weights2) + biases2
我将损失变量中的参数 logits 从 relu_out 更改为 logits_2。
loss = tf.reduce_mean(
tf.nn.softmax_cross_entropy_with_logits(logits=logits_2,labels=tf_train_labels))
最后,我更改了预测变量,以便使用 logits_2 而不是 relu_out 进行计算。
train_prediction = tf.nn.softmax(logits_2)
valid_prediction = tf.nn.softmax(
tf.matmul(tf.nn.relu(tf.matmul(tf_valid_dataset,weights1) +biases1), weights2) + biases2)
test_prediction = tf.nn.softmax(
tf.matmul(tf.nn.relu(tf.matmul(tf_test_dataset, weights1) + biases1), weights2) + biases2)
如您所见,准确率提高了 90% 左右
尽管我仍然不确定为什么两次实现 relu() 方法会出现问题。如果我没记错的话,relu() 方法返回 0 或给定参数的值,那么它不应该是相同的吗?
如果有人知道请回答
关于python - 我的神经网络没有提高其准确性,我们在Stack Overflow上找到一个类似的问题: https://stackoverflow.com/questions/47956261/