作者:CK92_474 | 来源:互联网 | 2023-02-05 20:26
1> Miriam Farbe..:
我将您的优化器更改为AdamOptimizer(在许多情况下,其性能都优于GradientDescentOptimizer
)。
我也玩了一些参数。特别是,对于变量初始化,我采用了较小的std,降低了学习速度(因为您的损失不稳定且“跳跃”),并且时期增加了(因为我注意到您的损失继续减少)。
我还减小了隐藏层的大小。如果没有太多数据,则很难训练具有较大隐藏层的网络。
关于您的损失,最好对其进行申请tf.reduce_mean
,以使损失成为一个数字。另外,按照ml4294的答案,我使用softmax代替了Sigmoid,所以损失看起来像:
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_,labels=y))
以下代码在训练数据上的准确性达到99.9%左右:
import numpy as np
import tensorflow as tf
sess = tf.InteractiveSession()
# generate data
np.random.seed(10)
inputs = np.random.normal(size=[1000,150]).astype('float32')*1.5
label = np.round(np.random.uniform(low=0,high=1,size=[1000,1])*0.8)
reverse_label = 1-label
labels = np.append(label,reverse_label,1)
# parameters
learn_rate = 0.002
epochs = 400
n_input = 150
n_hidden = 60
n_output = 2
# set weights/biases
x = tf.placeholder(tf.float32, [None, n_input])
y = tf.placeholder(tf.float32, [None, n_output])
b0 = tf.Variable(tf.truncated_normal([n_hidden],stddev=0.2,seed=0))
b1 = tf.Variable(tf.truncated_normal([n_output],stddev=0.2,seed=0))
w0 = tf.Variable(tf.truncated_normal([n_input,n_hidden],stddev=0.2,seed=0))
w1 = tf.Variable(tf.truncated_normal([n_hidden,n_output],stddev=0.2,seed=0))
# step function
def returnPred(x,w0,w1,b0,b1):
z1 = tf.add(tf.matmul(x, w0), b0)
a2 = tf.nn.relu(z1)
z2 = tf.add(tf.matmul(a2, w1), b1)
h = tf.nn.relu(z2)
return h #return the first response vector from the
y_ = returnPred(x,w0,w1,b0,b1) # predict operation
loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=y_,labels=y)) # calculate loss between prediction and actual
model = tf.train.AdamOptimizer(learning_rate=learn_rate).minimize(loss) # apply gradient descent based on loss
init = tf.global_variables_initializer()
tf.Session = sess
sess.run(init) #initialize graph
for step in range(0,epochs):
sess.run([model,loss],feed_dict={x: inputs, y: labels }) #train model
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
print(sess.run(accuracy, feed_dict={x: inputs, y: labels})) # print accuracy