Python Tensorflow中的人脸识别系统

Question

So I decided to go further on the MNIST tutorial in Google's Tensorflow and try to create a rudimentary face recognition system.

The Directories:

amar -> contains all the target images

test -> contains all the testing images with negatives

train -> contains all the training images

There are 60 image files in each directory. I am using the directory names as the image labels.

At this point I am able to extract the images intensities and everything is done but I am getting the following error:

I tensorflow/core/common_runtime/local_device.cc:40] Local device intra op parallelism threads: 4
I tensorflow/core/common_runtime/direct_session.cc:58] Direct session inter op parallelism threads: 4
[[0, 0, 1], [0, 0, 1]]
Traceback (most recent call last):
  File "face.py", line 82, in <module>
    model()
  File "face.py", line 74, in model
    sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})
  File "/usr/local/lib/python2.7/dist-packages/tensorflow/python/client/session.py", line 357, in run
    np_val = np.array(subfeed_val, dtype=subfeed_t.dtype.as_numpy_dtype)
ValueError: setting an array element with a sequence.

The following is the code:

def preprocessImages(dir):      # Grescaling the images
    from os import listdir
    from os.path import isfile, join
    import Image    
    path = dir
    onlyfiles = [f for f in listdir(path) if isfile(join(path, f))]
    for files in onlyfiles:
        img = Image.open(str(path+files)).convert('L')
        img.save(str(path+files))

def extractImages(path):        # Extracting image pixel intensities in an array
    images = []
    from os import listdir
    from os.path import isfile, join
    import Image
    onlyfiles = [f for f in listdir(path) if isfile(join(path, f))]
    for image in onlyfiles:
        img = Image.open(path+image)
        pixVal = list(img.getdata())
        images.append(pixVal)
    return images

def extractLabels(target):      # Extracting labels or directories accordingly 
    res = []
    path = './'
    from os import listdir
    from os.path import isfile, join
    import Image    
    onlyfiles = [f for f in listdir(path) if not isfile(join(path, f))]
    for i in onlyfiles:
        if i == target:
            res.append(1)
        else:
            res.append(0)
    return res
dirs = ['train','test','amar']  # put some directories here
labels = []
images = []

for di in dirs:
    labels.append(extractLabels(di))        
    images.append(extractImages(('./'+di+'/')))

def batch(no):          # Function to select a batch of elements from both the arrays
    import random
    import numpy as np
    global labels
    global images
    lab = []
    img = []
    for i in range(no):
        lab.append(random.choice(labels))
    for i in range(no):
        img.append(random.choice(images))
    return img,lab

def model():
    import tensorflow as tf
    x = tf.placeholder(tf.float32, [None,409600])       # The images of 240x240 = 409600 pixels
    W = tf.Variable(tf.zeros([409600,3]))           # The weights for each image 
    b = tf.Variable(tf.zeros([3]))              # The labels of the images containing the real numbers
    y = tf.nn.softmax(tf.matmul(x, W) + b)          # The predicted y viz. y = softmax(W*x + b)
    y_ = tf.placeholder(tf.float32, [None, 3])      # The real y that will be checked against the prediction
    cross_entropy = -tf.reduce_sum(y_*tf.log(y))        # The entropy error b/w the y and y_
    train_step = tf.train.GradientDescentOptimizer(0.01).minimize(cross_entropy)    # The method to optimize error at each training step
    init = tf.initialize_all_variables()            # initializing all variables
    sess = tf.Session()
    sess.run(init)

    # Training for our model
    for i in range(1000):                   
      batch_xs, batch_ys = batch(2)
      print batch_ys
      sess.run(train_step, feed_dict={x: batch_xs, y_: batch_ys})

    correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))      # Checking for the prediction
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"))     # Accuracy of the prediction
    accuracy = sess.run(accuracy, feed_dict={x: extractImages('./test/'), y_: extractLabels('test')})*100   # Normalizing it in terms of percentage
    print "The accuracy of the model was",accuracy," %"
    print type(mnist)

model()

Answer 1

batch_xs和batch_ys（通常是feed_dict的输入）应该是numpy数组，而不是列表。

Answer 2

In your Batch function you are returning image,label and both are list types. My Suggestion is that you convert this images and labels into np.array right before returning it. something like this

def batch(no):
     do the batch calculation and prepare the img,label set
     # now before returning convert then to np array
     img = np.array(img)
     return img, label

I tried your batch function in my code and this is how I was able to solve the error.

Hope this will solve your error.