人臉識別系統在Python Tensorflow

Question

所以我決定再進一步在谷歌的Tensorflow的MNIST tutorial，並嘗試建立一個基本的人臉識別系統。

目錄：

阿馬爾 - >包含了所有的目標圖像

測試 - >包含了所有的測試圖片與底片

列車 - >包含了所有的訓練圖像

有每個目錄中有60個圖像文件。我使用目錄名稱作爲圖像標籤。

在這一點上，我能提取圖像亮度，一切都做，但我收到以下錯誤：

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. 

以下是代碼：

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 （和一般輸入到feed_dict）預計將numpy的陣列，而不是列表。

Answer 2

在批處理功能，您正在返回圖像，標籤，無一不是列表類型。 我的建議是，你返回之前轉換該圖像和標籤到np.array權。 像這樣

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 

我在我的代碼試圖您的批處理功能，這是我能夠解決的錯誤。

希望這將解決您的錯誤。