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我与tensorflow卷积神经网络，我训练它，测试它（约98％的准确率）...我保存为什么我在这个神经网络（tensorflow）中得到低精度？
saver = tf.train.Saver()
saver.save(sess, 'model.ckpt')
那么模型我金丹恢复，但我总是得到低于50％的准确度......为什么？ 下面的代码：
import tensorflow as tf
import matplotlib.pyplot as plt
import pickle
import numpy as np
with open('X_train.pickle', 'rb') as y:
u = pickle._Unpickler(y)
u.encoding = 'latin1'
X_train = u.load()
with open('X_test.pickle', 'rb') as y:
u = pickle._Unpickler(y)
u.encoding = 'latin1'
X_test = u.load()
X_test = np.array(X_test).reshape(-1, 2500)
with open('y_train.pickle', 'rb') as y:
u = pickle._Unpickler(y)
u.encoding = 'latin1'
y_train = u.load()
with open('y_test.pickle', 'rb') as y:
u = pickle._Unpickler(y)
u.encoding = 'latin1'
y_test = u.load()
n_classes = 3
batch_size = 100
x = tf.placeholder('float', [None, 2500])
y = tf.placeholder('float')
keep_rate = 0.8
keep_prob = tf.placeholder(tf.float32)
def conv2d(x, W):
return tf.nn.conv2d(x, W, strides=[1,1,1,1], padding='SAME')
def maxpool2d(x):
# size of window movement of window
return tf.nn.max_pool(x, ksize=[1,2,2,1], strides=[1,2,2,1], padding='SAME')
def convolutional_neural_network(x):
weights = {'W_conv1':tf.Variable(tf.random_normal([5,5,1,32])),
'W_conv2':tf.Variable(tf.random_normal([5,5,32,64])),
'W_fc':tf.Variable(tf.random_normal([13*13*64,1024])),
'out':tf.Variable(tf.random_normal([1024, n_classes]))}
biases = {'b_conv1':tf.Variable(tf.random_normal([32])),
'b_conv2':tf.Variable(tf.random_normal([64])),
'b_fc':tf.Variable(tf.random_normal([1024])),
'out':tf.Variable(tf.random_normal([n_classes]))}
x = tf.reshape(x, shape=[-1, 50, 50, 1])
conv1 = tf.nn.relu(conv2d(x, weights['W_conv1']) + biases['b_conv1'])
conv1 = maxpool2d(conv1)
conv2 = tf.nn.relu(conv2d(conv1, weights['W_conv2']) + biases['b_conv2'])
conv2 = maxpool2d(conv2)
fc = tf.reshape(conv2,[-1, 13*13*64])
fc = tf.nn.relu(tf.matmul(fc, weights['W_fc'])+biases['b_fc'])
#fc = tf.nn.dropout(fc, keep_rate)
output = tf.matmul(fc, weights['out'])+biases['out']
return output
def use_neural_network(input_data):
prediction = convolutional_neural_network(x)
sess.run(tf.global_variables_initializer())
result = (sess.run(tf.argmax(prediction.eval(feed_dict={x:[input_data]}),1)))
correct = tf.equal(tf.argmax(prediction, 1), tf.argmax(y, 1))
accuracy = tf.reduce_mean(tf.cast(correct, 'float'))
print('Accuracy:',accuracy.eval({x:X_test, y:y_test}))
return result
with tf.Session() as sess:
c = convolutional_neural_network(x)
saver = tf.train.Saver()
saver.restore(sess, "model.ckpt")
sample = X_train[432].reshape(2500)
res = use_neural_network(sample)
if res == [0]: print('Go straight')
elif res == [1]: print('Turn right')
else: print('Turn left')
img = sample.reshape(50,50)
plt.imshow(img)
plt.show()
sample = X_train[1222].reshape(2500)
res = use_neural_network(sample)
if res == [0]: print('Go straight')
elif res == [1]: print('Turn right')
else: print('Turn left')
img = sample.reshape(50,50)
plt.imshow(img)
plt.show()
sample = X_train[2986].reshape(2500)
res = use_neural_network(sample)
if res == [0]: print('Go straight')
elif res == [1]: print('Turn right')
else: print('Turn left')
img = sample.reshape(50,50)
plt.imshow(img)
plt.show()
的问题不能过学习，因为我与训练数据集的元素测试它... 我敢肯定，这个问题是金丹，但我无法弄清楚如何解决它...
您的加载模型是不正确的。我已经回答了它：https：//stackoverflow.com/questions/44873204/how-to-predict-with-meta-and-checkpoint-files-in-tensorflow/44876333#44876333 –