fashion_mnist识别准确率一般为多少呢？我看好多人都是92%左右，但是我用一个网络达到了94%，想问问做过的小伙伴到底是多少？
#这是我的结果示意
x_shape: (60000, 28, 28)
y_shape: (60000,)
epoches: 0 val_acc: 0.4991 train_acc 0.50481665
epoches: 1 val_acc: 0.6765 train_acc 0.66735
epoches: 2 val_acc: 0.755 train_acc 0.7474
epoches: 3 val_acc: 0.7846 train_acc 0.77915
epoches: 4 val_acc: 0.798 train_acc 0.7936
epoches: 5 val_acc: 0.8082 train_acc 0.80365
epoches: 6 val_acc: 0.8146 train_acc 0.8107
epoches: 7 val_acc: 0.8872 train_acc 0.8872333
epoches: 8 val_acc: 0.896 train_acc 0.89348334
epoches: 9 val_acc: 0.9007 train_acc 0.8986
epoches: 10 val_acc: 0.9055 train_acc 0.90243334
epoches: 11 val_acc: 0.909 train_acc 0.9058833
epoches: 12 val_acc: 0.9112 train_acc 0.90868336
epoches: 13 val_acc: 0.9126 train_acc 0.91108334
epoches: 14 val_acc: 0.9151 train_acc 0.9139
epoches: 15 val_acc: 0.9172 train_acc 0.91595
epoches: 16 val_acc: 0.9191 train_acc 0.91798335
epoches: 17 val_acc: 0.9204 train_acc 0.91975
epoches: 18 val_acc: 0.9217 train_acc 0.9220333
epoches: 19 val_acc: 0.9252 train_acc 0.9234667
epoches: 20 val_acc: 0.9259 train_acc 0.92515
epoches: 21 val_acc: 0.9281 train_acc 0.9266667
epoches: 22 val_acc: 0.9289 train_acc 0.92826664
epoches: 23 val_acc: 0.9301 train_acc 0.93005
epoches: 24 val_acc: 0.9315 train_acc 0.93126667
epoches: 25 val_acc: 0.9322 train_acc 0.9328
epoches: 26 val_acc: 0.9331 train_acc 0.9339667
epoches: 27 val_acc: 0.9342 train_acc 0.93523335
epoches: 28 val_acc: 0.9353 train_acc 0.93665
epoches: 29 val_acc: 0.9365 train_acc 0.9379333
epoches: 30 val_acc: 0.9369 train_acc 0.93885
epoches: 31 val_acc: 0.9387 train_acc 0.9399
epoches: 32 val_acc: 0.9395 train_acc 0.9409
epoches: 33 val_acc: 0.94 train_acc 0.9417667
epoches: 34 val_acc: 0.9403 train_acc 0.94271666
epoches: 35 val_acc: 0.9409 train_acc 0.9435167
epoches: 36 val_acc: 0.9418 train_acc 0.94443333
epoches: 37 val_acc: 0.942 train_acc 0.94515
epoches: 38 val_acc: 0.9432 train_acc 0.9460667
epoches: 39 val_acc: 0.9443 train_acc 0.9468833
epoches: 40 val_acc: 0.9445 train_acc 0.94741666
epoches: 41 val_acc: 0.9462 train_acc 0.9482
epoches: 42 val_acc: 0.947 train_acc 0.94893336
epoches: 43 val_acc: 0.9472 train_acc 0.94946665
epoches: 44 val_acc: 0.948 train_acc 0.95028335
epoches: 45 val_acc: 0.9486 train_acc 0.95095
epoches: 46 val_acc: 0.9488 train_acc 0.9515833
epoches: 47 val_acc: 0.9492 train_acc 0.95213336
epoches: 48 val_acc: 0.9495 train_acc 0.9529833
epoches: 49 val_acc: 0.9498 train_acc 0.9537
val_acc: 0.9498
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
def to_onehot(y,num):
lables = np.zeros([num,len(y)])
for i in range(len(y)):
lables[y[i],i] = 1
return lables.T
# 预处理数据
mnist = keras.datasets.fashion_mnist
(train_images,train_lables),(test_images,test_lables) = mnist.load_data()
print('x_shape:',train_images.shape)
#(60000)
print('y_shape:',train_lables.shape)
X_train = train_images.reshape((-1,train_images.shape[1]*train_images.shape[1])) / 255.0
#X_train = tf.reshape(X_train,[-1,X_train.shape[1]*X_train.shape[2]])
Y_train = to_onehot(train_lables,10)
X_test = test_images.reshape((-1,test_images.shape[1]*test_images.shape[1])) / 255.0
Y_test = to_onehot(test_lables,10)
#双隐层的神经网络
input_nodes = 784
output_nodes = 10
layer1_nodes = 100
layer2_nodes = 50
batch_size = 100
learning_rate_base = 0.8
learning_rate_decay = 0.99
regularization_rate = 0.0000001
epochs = 50
mad = 0.99
learning_rate = 0.005
# def inference(input_tensor,avg_class,w1,b1,w2,b2):
# if avg_class == None:
# layer1 = tf.nn.relu(tf.matmul(input_tensor,w1)+b1)
# return tf.nn.softmax(tf.matmul(layer1,w2) + b2)
# else:
# layer1 = tf.nn.relu(tf.matmul(input_tensor,avg_class.average(w1)) + avg_class.average(b1))
# return tf.matual(layer1,avg_class.average(w2)) + avg_class.average(b2)
def train(mnist):
X = tf.placeholder(tf.float32,[None,input_nodes],name = "input_x")
Y = tf.placeholder(tf.float32,[None,output_nodes],name = "y_true")
w1 = tf.Variable(tf.truncated_normal([input_nodes,layer1_nodes],stddev=0.1))
b1 = tf.Variable(tf.constant(0.1,shape=[layer1_nodes]))
w2 = tf.Variable(tf.truncated_normal([layer1_nodes,layer2_nodes],stddev=0.1))
b2 = tf.Variable(tf.constant(0.1,shape=[layer2_nodes]))
w3 = tf.Variable(tf.truncated_normal([layer2_nodes,output_nodes],stddev=0.1))
b3 = tf.Variable(tf.constant(0.1,shape=[output_nodes]))
layer1 = tf.nn.relu(tf.matmul(X,w1)+b1)
A2 = tf.nn.relu(tf.matmul(layer1,w2)+b2)
A3 = tf.nn.relu(tf.matmul(A2,w3)+b3)
y_hat = tf.nn.softmax(A3)
# y_hat = inference(X,None,w1,b1,w2,b2)
# global_step = tf.Variable(0,trainable=False)
# variable_averages = tf.train.ExponentialMovingAverage(mad,global_step)
# varible_average_op = variable_averages.apply(tf.trainable_variables())
#y = inference(x,variable_averages,w1,b1,w2,b2)
cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=A3,labels=Y))
regularizer = tf.contrib.layers.l2_regularizer(regularization_rate)
regularization = regularizer(w1) + regularizer(w2) +regularizer(w3)
loss = cross_entropy + regularization * regularization_rate
# learning_rate = tf.train.exponential_decay(learning_rate_base,global_step,epchos,learning_rate_decay)
# train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss,global_step=global_step)
train_step = tf.train.GradientDescentOptimizer(learning_rate).minimize(loss)
# with tf.control_dependencies([train_step,varible_average_op]):
# train_op = tf.no_op(name="train")
correct_prediction = tf.equal(tf.argmax(y_hat,1),tf.argmax(Y,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction,tf.float32))
total_loss = []
val_acc = []
total_train_acc = []
x_Xsis = []
with tf.Session() as sess:
tf.global_variables_initializer().run()
for i in range(epochs):
# x,y = next_batch(X_train,Y_train,batch_size)
batchs = int(X_train.shape[0] / batch_size + 1)
loss_e = 0.
for j in range(batchs):
batch_x = X_train[j*batch_size:min(X_train.shape[0],j*(batch_size+1)),:]
batch_y = Y_train[j*batch_size:min(X_train.shape[0],j*(batch_size+1)),:]
sess.run(train_step,feed_dict={X:batch_x,Y:batch_y})
loss_e += sess.run(loss,feed_dict={X:batch_x,Y:batch_y})
# train_step.run(feed_dict={X:x,Y:y})
validate_acc = sess.run(accuracy,feed_dict={X:X_test,Y:Y_test})
train_acc = sess.run(accuracy,feed_dict={X:X_train,Y:Y_train})
print("epoches: ",i,"val_acc: ",validate_acc,"train_acc",train_acc)
total_loss.append(loss_e / batch_size)
val_acc.append(validate_acc)
total_train_acc.append(train_acc)
x_Xsis.append(i)
validate_acc = sess.run(accuracy,feed_dict={X:X_test,Y:Y_test})
print("val_acc: ",validate_acc)
return (x_Xsis,total_loss,total_train_acc,val_acc)
result = train((X_train,Y_train,X_test,Y_test))
def plot_acc(total_train_acc,val_acc,x):
plt.figure()
plt.plot(x,total_train_acc,'--',color = "red",label="train_acc")
plt.plot(x,val_acc,color="green",label="val_acc")
plt.xlabel("Epoches")
plt.ylabel("acc")
plt.legend()
plt.show()
