我现在学习tensorflow和python。我试图改变MIT youtube CIFAR10教程代码的某些部分。
（ https://github.com/Hvass-Labs/TensorFlow-Tutorials ）
我稍微更改了代码以进行研究，但它显示我的准确率低于15％
我之前尝试过很多次错误，但我无法做到。我在这里待了2天多。
这是我的代码
import tensorflow as tf
import numpy as np
np.set_printoptions(threshold=np.nan)
import matplotlib.pyplot as plt
import matplotlib.cm as cm
import preprocessingFunction as pf
import helperFunctions as hf
import os
import time
from datetime import timedelta
'''----------------------------------------------------------------
------------Get one image from training sets--------------------
---------------------------------------------------------------'''
from tensorflow.examples.tutorials.mnist import input_data
mnist = input_data.read_data_sets("MNSIT_data/", one_hot=True)
print ( mnist.train.images.shape , mnist.train.labels.shape)
images_train = (mnist.train.images).reshape(-1,28,28,1)
labels_train = mnist.train.labels
print (mnist.test.images.shape, mnist.test.labels.shape)
mnist.test.cls = np.array([label.argmax() for label in mnist.test.labels])
'''----------------------------------------------------------------
------------Get one image from training sets--------------------
---------------------------------------------------------------'''
img_3 = mnist.train.images[2]
print ('Third image size is :' , img_3.shape )
img_3_reshaped = mnist.train.images[2].reshape( (28,28) )
plt.imshow(img_3_reshaped, cmap = cm.Greys)
#plt.show()
print('Reshaped image size is :', img_3_reshaped.shape, mnist.train.labels[2] )
'''
# Inverse the image and print it, just checking the inverse algorithm...
img_3_reshaped = pf.inverseImageBW_array(img_3_reshaped)
print('Reshaped inversed image size is :', img_3_reshaped.shape, mnist.train.labels[2] )
plt.imshow(img_3_reshaped, cmap = cm.Greys)
plt.show()
'''
# Tensor variables
x_reshape = tf.placeholder(tf.float32, shape = [None, 28,28,1])
#x_reshape = tf.reshape(x, [-1,28,28,1])
y_ = tf.placeholder(tf.float32, [None,10])
y_true_cls = tf.argmax(y_, dimension = 1)
# Make image distorted
#distorted_images = pf.pre_process(images = x_reshape, training = True)
'''----------------------------------------------------------------
-------Main conv network with conv filters, fully cntd----------
----------------------------------------------------------------'''
def main_conv_nn(images, training):
# Convolution
convFilterShape = [5, 5, 1, 32]
convFilterWeights = tf.Variable(tf.truncated_normal(convFilterShape, stddev=0.05))
conv2d = tf.nn.conv2d(x_reshape, filter=convFilterWeights, strides=[1, 1, 1, 1], padding='SAME')
print(conv2d)
# MAX POOL
conv2d = tf.nn.max_pool(value=conv2d, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
layer_shape = conv2d.get_shape()
print('layer shape is ', layer_shape)
num_features = layer_shape[1:4].num_elements()
print('num_features are ', num_features)
# Flatten
layer_flat = tf.reshape(conv2d, [-1, num_features])
# Fully connected Layer
W = tf.Variable(tf.truncated_normal([num_features,10]))
b = tf.Variable(tf.truncated_normal( shape= [10]))
y = tf.nn.softmax(tf.matmul(layer_flat, W) + b)
print('tensor variable softmax y looks like : ' ,y.get_shape())
'''
train_step = tf.train.GradientDescentOptimizer(0.5).minimize(loss_cross_entropy)
tf.global_variables_initializer().run()
'''
loss_cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y))
return y, loss_cross_entropy, layer_flat, conv2d
def build_conv_nn(training):
with tf.variable_scope('network', reuse= not training):
# Rename image
images = x_reshape
# Image distortion
#images = pf.pre_process(images= images, training = training)
# Create tensorflow graph
y, loss, layer_flat, conv2d = main_conv_nn(images= images, training = training)
return y, loss,layer_flat, conv2d
global_step = tf.Variable(initial_value= 0, name = 'global_step', trainable= False)
# At first, build convolution NN for TRAINING & set optimizer
_, loss,_,_ = build_conv_nn(training= True)
optimizer = tf.train.GradientDescentOptimizer(learning_rate=1e-6).minimize(loss, global_step=global_step)
# Secondly, build convolution NN for TEST, in this case, we don't need optimizer
y_prediction, _,layer_flat, conv2d = build_conv_nn(training= False)
y_prediction_cls = tf.argmax(y_prediction, dimension = 1)
# If we get y_prediction_cls, then we can compare this with y_true_cls
# y_true_cls will be given by test set labels
correct_prediction = tf.equal(y_prediction_cls, y_true_cls)
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
saver = tf.train.Saver()
# Session START
# we use this session to run our graph
sess = tf.Session()
save_dir = 'checkpoints/'
if not os.path.exists(save_dir):
os.makedirs(save_dir)
save_path = os.path.join(save_dir,'main')
try:
print("Trying to restore last checkPoint ...")
last_chk_path = tf.train.latest_checkpoint(checkpoint_dir=save_dir)
saver.restore(sess, save_path = last_chk_path)
except:
# Actually, we do not need tf.global_variables_initializer, but failing, we do
print("Failed to restore checkpoint. Initializing variables insted.")
sess.run(tf.global_variables_initializer())
train_batch_size = 128
# Just return index'th image
def random_batch():
number_of_images = len(images_train)
index = np.random.choice(number_of_images, size = train_batch_size, replace= False)
x_batch = images_train[index, :, :, :]
y_batch = labels_train[index, :]
return x_batch, y_batch
def optimize(number_of_iterations):
start_time = time.time() # start time alloc
for i in range(number_of_iterations):
# randomly get batches
x_batch, y_true_batch = random_batch()
feed_dictionary_train = { x_reshape: x_batch, y_: y_true_batch } # y_ = y_true : we will take real y_true one_hot_encoding vector
i_global, _ = sess.run([global_step, optimizer] , feed_dict= feed_dictionary_train) # At first, we will run optimizer, also global_step will be increased
if( i_global %100 ==0) or (i == number_of_iterations -1 ) :
batch_acc = sess.run(accuracy, feed_dict= feed_dictionary_train)
print(' conv2d is : ', sess.run(conv2d, feed_dict=feed_dictionary_train))
print(' layer_flat is : ', sess.run(layer_flat, feed_dict=feed_dictionary_train))
print('y prediction is : ' ,sess.run(y_prediction, feed_dict=feed_dictionary_train))
print('y prediction cls is : ' ,sess.run(y_prediction_cls, feed_dict=feed_dictionary_train))
print('y true cls is :', sess.run(y_true_cls, feed_dict=feed_dictionary_train))
msg = "Global step: {0:>6}, Training Batch Accuracy: {1:>6.1%}"
print(msg.format(i_global, batch_acc)) # batch_acc will return accuracy.
if(i_global%1000 == 0) or(i == number_of_iterations -1):
saver.save(sess, save_path=save_path, global_step = global_step)
print("saved checkpoint...")
end_time = time.time()
time_diff = end_time - start_time
print("Time usage: " + str(timedelta(seconds = int(round(time_diff)))))
if True:
optimize(number_of_iterations= 2500)
def plot_distorted_image(image, cls_true):
# Repeat the input image 9 times.
image = np.array(image)
image_duplicates = np.repeat(image[np.newaxis], 9, axis=0)
# Create a feed-dict for TensorFlow.
feed_dict = {x_reshape: image_duplicates}
# Calculate only the pre-processing of the TensorFlow graph
# which distorts the images in the feed-dict.
result = sess.run(distorted_images, feed_dict=feed_dict)
# Plot the images.
#hf.plot_images(images=result, cls_true=np.repeat(cls_true, 9))
'''
def get_test_image(i):
return (mnist.test.images)[i].reshape(28,28,1), (mnist.test.cls)[i]
img, cls = get_test_image(13)
plot_distorted_image(img, cls)
'''
sess.close()
我还尝试多次更改学习率，并且还更改了W的初始化值。没有解决问题。 代码有什么问题？