# import tensorflow as tf
# dnn 神经网络
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# x = tf.Variable(3, name="x")
# y = tf.Variable(4, name="y")
# f = x*x*y + y + 2
# # way1
# sess = tf.Session()
#
sess.run(x.initializer)
#
sess.run(y.initializer)
# result =
sess.run(f)
# print(result)
# sess.close()
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
from sklearn.metrics import accuracy_score
import numpy as np
if __name__ == '__main__':
n_inputs = 28 * 28
n_hidden1 = 300
n_hidden2 = 100
n_outputs = 10
mnist = input_data.read_data_sets("tmp/data/")
X_train = mnist.train.images
X_test = mnist.test.images
y_train = mnist.train.labels.astype("int")
y_test = mnist.test.labels.astype("int")
X = tf.placeholder(tf.float32, shape= (None, n_inputs), name='X')
y = tf.placeholder(tf.int64, shape=(None), name = 'y')
with tf.name_scope('dnn'):
hidden1 = tf.layers.dense(X, n_hidden1, activation=tf.nn.relu
,name= 'hidden1')
hidden2 = tf.layers.dense(hidden1, n_hidden2, name='hidden2',
activation= tf.nn.relu)
logits = tf.layers.dense(hidden2, n_outputs, name='outputs')
with tf.name_scope('loss'):
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels = y,
logits = logits)
loss = tf.reduce_mean(xentropy, name='loss')#所有值求平均
learning_rate = 0.01
with tf.name_scope('train'):
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
training_op = optimizer.minimize(loss)
with tf.name_scope('eval'):
correct = tf.nn.in_top_k(logits ,y ,1)#是否与真值一致 返回布尔值
accuracy = tf.reduce_mean(tf.cast(correct, tf.float32)) #tf.cast 将数据转化为 0,1 序列
init = tf.global_variables_initializer()
n_epochs = 20
batch_size = 50
# with tf.Session() as sess:
# saver = tf.train.Saver()
#
init.run()
# for epoch in range(n_epochs):
# for iteration in range(mnist.train.num_examples // batch_size):
# X_batch, y_batch = mnist.train.next_batch(batch_size)
#
sess.run(training_op,feed_dict={X:X_batch,
# y: y_batch})
# acc_train = accuracy.eval(feed_dict={X:X_batch,
# y: y_batch})
# acc_test = accuracy.eval(feed_dict={X: mnist.test.images,
# y: mnist.test.labels})
# print(X_batch.shape)
# print(epoch, "Train accuracy:", acc_train, "Test accuracy:", acc_test)
# # # saver.restore(sess, "./my_model_final_mnist.ckpt") # or better, use save_path
# save_path = saver.save(sess, "./tensor9/my_model_final.ckpt")
from PIL import Image, ImageFilter
# import tensorflow as tf
def imageprepare():
file_name = './5.png' # 图片路径
myimage = Image.open(file_name).convert('L') # 转换成灰度图
tv = list(myimage.getdata()) # 获取像素值
# 转换像素范围到[0 1], 0 是纯白 1 是纯黑
tva = [(255-x)*1.0/255.0 for x in tv]
# print(tva)
tva = np.array(tva)
# print(tva)
return tva
result = imageprepare().reshape(1,784)
print(mnist.test.images.shape)
print(result.reshape(1,784).shape)
# init = tf.global_variables_initializer()
# saver = tf.train.Saver
with tf.Session() as sess:
sess.run(init)
saver = tf.train.import_meta_graph('./tensor9/my_model_final.ckpt.meta') # 载入模型结构
saver.restore(sess, './tensor9/my_model_final.ckpt') # 载入模型参数
y = tf.nn.softmax(y) # 为了打印出预测值，我们这里增加一步通过 softmax 函数处理后来输出一个向量
# y = tf.cast(y, tf.int64)
# result =
sess.run(y, feed_dict={X: result})
# graph = tf.get_default_graph() # 计算图
# # x = graph.get_tensor_by_name("x:0") # 从模型中获取张量 x
# y = graph.get_tensor_by_name("y:0") # 从模型中获取张量 y
# y = tf.placeholder(tf.int64, shape=(None), name = 'y')
X = tf.placeholder(tf.float32, shape= (None, n_inputs), name='X')
# y =
prediction = tf.argmax(y, 1)
predint = prediction.eval(feed_dict={X: result}, session=sess)
print(result)