Python 如何在“tensorflow”中修正精度函数？

我在评估正确的预测时出错

这是我的全部代码：

def compute_accuracy(testimgName , testimgLabel ):
    global prediction
    v_xs = np.empty([1,224,224], dtype=np.int)
    v_ys = np.empty([1,8], dtype=np.float32)
    g = testimgName[1] 
    v_xs = np.array(Image.open(path+g))
    v_xs = np.reshape(v_xs,[1,224,224])
    v_ys = testimgLabel[1] ## It is a list = ['0', '0', '0', '0', '0', '0', '1', '0']
    v_ys = np.reshape(v_ys , [1,8])##print(v_ys) = [['0' '0' '0' '0' '0' '0' '1' '0']]
    y_pre = sess.run(prediction, feed_dict={xs: v_xs, keep_prob: 1})
    correct_prediction = tf.equal(tf.argmax(y_pre,1), tf.argmax(v_ys,1))
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
    result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})
    return result
def weight_variable(shape):
    initial = tf.truncated_normal(shape , stddev=0.1)
    return tf.Variable(initial)

def bias_variable(shape):
    initial = tf.constant(0.1,shape=shape)
    return tf.Variable(initial)

def conv2d(x, W):
    #stride [1,x_movment,y_movment,1]
    #Must have strides[0] = strides[3] = 1
    return tf.nn.conv2d(x,W,strides=[1,1,1,1],padding='SAME') #stride [batch,height,width,channels]

def max_pool_2x2(x):
    #stride[1,x_movment,y_movment ,1]
    return tf.nn.max_pool(x,ksize=[1,2,2,1],strides=[1,2,2,1], padding='SAME')

# size of image
image_height = 224
image_width = 224
number_of_class = 8
# define placeholder for inputs to network
xs = tf.placeholder(tf.float32, [None, 224, 224]) # 224*224
ys = tf.placeholder(tf.float32, [None, 8])
keep_prob = tf.placeholder(tf.float32)
x_image = tf.reshape(xs,[-1,224,224,1])

## conv1 layer ##
W_conv1 = weight_variable([5,5,1,32])
b_conv1 = bias_variable([32])
h_conv1 = tf.nn.relu(conv2d(x_image,W_conv1)+ b_conv1) #output size 224*224*32 
h_pool1 = max_pool_2x2(h_conv1) # 112*112*32

## conv2 layer ##
W_conv2 = weight_variable([5,5,32,64])
b_conv2 = bias_variable([64])
h_conv2 = tf.nn.relu(conv2d(h_pool1,W_conv2)+ b_conv2) #output size 112*112*64 
h_pool2 = max_pool_2x2(h_conv2) # output size of pooling 56*56*64

## conv3 layer ##
W_conv3 = weight_variable([5,5,64,64])
b_conv3 = bias_variable([64])
h_conv3 = tf.nn.relu(conv2d(h_pool2,W_conv3)+ b_conv3) #output size 56*56*64 
h_pool3 = max_pool_2x2(h_conv3) # output size of pooling 28*28*64

## conv4 layer ##
W_conv4 = weight_variable([5,5,64,32])
b_conv4 = bias_variable([32])
h_conv4 = tf.nn.relu(conv2d(h_pool3,W_conv4)+ b_conv4) #output size 28*28*32
h_pool4 = max_pool_2x2(h_conv4) # output size of pooling 14*14*32

## func1 layer ##
W_fc1 = weight_variable([14*14*32,256])
b_fc1 = bias_variable([256])

#[n_sample, 14,14,32] ->> [n_sample,14*14*32]
h_pool4_flat = tf.reshape(h_pool4,[-1,14*14*32])
h_fc1 = tf.nn.relu(tf.matmul(h_pool4_flat,W_fc1) + b_fc1)
h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)

## func2 layer ##
W_fc2 = weight_variable([256,8])
b_fc2 = bias_variable([8])
prediction = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2) + b_fc2)


# the error between prediction and real data
cross_entropy = tf.reduce_mean(-tf.reduce_sum(ys * tf.log(prediction),
                                          reduction_indices=[1]))   # loss
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy)

sess = tf.Session()
sess.run(tf.global_variables_initializer())
trainimgNames , trainimgLabels = ut.getlabel(path,textFileName_train)
imgTestNames , imgTestLabels = ut.getlabel(path,textFileName_test)
batch_size = 1
number_of_batches = len(trainimgNames)/batch_size
for i in range(int(number_of_batches)):
    batch_x = trainimgNames[i*batch_size:i*batch_size+batch_size]
    batch_y = trainimgLabels[i*batch_size:i*batch_size+batch_size]
    batch_image_data = np.empty([batch_size,image_height,image_width], dtype=np.int)
    for j in range(len(batch_x)):
        f = batch_x[j]
        batch_image_data[j] = np.array(Image.open(path+f))
    sess.run(train_step, feed_dict={xs: batch_image_data, ys:batch_y, keep_prob: 0.5})
    print(compute_accuracy(imgTestName , imgTestLabel))        

而getlabel是：

当我调用printcomputer_Accurance时，会出现错误

错误是

---

我不知道为什么y_pre会这样，预测会出错。有人能帮我修复它吗？

TensorFlow的哲学与纯Python不同。首先定义张量，然后运行它

在第一部分中，您定义了预测，但没有运行任何内容。它是声明性的。您声明预测取决于不同的变量/操作

仅当您调用sess.run时才会进行计算。因此，y_pres不是张量，它是一个python变量numpy，可能包含结果。因此，不应该将其传递给TF函数

那又怎样？因此，您应该将Tensorflow声明性部分与运行部分分开。这意味着，只保留第二个sess.run调用，此调用和第一个sess.run之间的所有内容都属于声明性部分。它应该看起来有点像：

免责声明：下面的代码甚至不起作用，请参见问题1或其他问题

定义张量的声明性部分，即TF的计算图：

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您的代码不完整。您的代码段中未定义预测。请提供一个完整的和可复制的例子。是的，你是对的，我应该写它@所以下面的答案是正确的；您应该将定义Tensorflow图的代码与运行它的代码分开。至于您的具体问题，似乎计算精度中的一个或多个numpy数组或张量是string类型的。如果我读对了代码，那么标签值就是字符串。你应该把它们转换成数字。请看mnist_softmax.py示例：。希望有帮助！我刚刚编辑了我的代码。这是全部代码。你能再看看吗@PLTRDY然后您应该应用我建议的更改，即删除1个sess.run。您在sess.run返回的numpy对象上调用TF函数这一事实一定会引起麻烦。@amirMaleki情况如何？正如您所说的@pltrdy我更改了代码，但它工作不正确，我正在尝试修复发生的语义错误。我很感激你

def getlabel(path,txtFileName):
    names = []
    labels = []
    dirs = os.listdir( path )

    with open(txtFileName, 'r') as f:
    for line in f.readlines():
        tokens = line.split(' ')
        names.append(tokens[0])
        labels.append(int(tokens[1]))
    i = 0
    listLabel= []
    f = []
    for item in labels:
        a = ('{0:08b}'.format(labels[i]))
        listLabel.append(list(a))
        i+=1
    return names,listLabel 

TypeError: DataType string for attr 'T' not in list of allowed values: float32, float64, int64, int32, uint8, uint16, int16, int8, complex64, complex128, qint8, quint8, qint32, float16

  v_ys = tf.Placeholder( <type> )  

  W_fc1 = weight_variable([14*14*32,256])
  b_fc1 = bias_variable([256])
  h_pool4_flat = tf.reshape(h_pool4,[-1,14*14*32])
  h_fc1 = tf.nn.relu(tf.matmul(h_pool4_flat,W_fc1) + b_fc1)
  h_fc1_drop = tf.nn.dropout(h_fc1,keep_prob)
  W_fc2 = weight_variable([256,8])
  b_fc2 = bias_variable([8])
  prediction = tf.nn.softmax(tf.matmul(h_fc1_drop,W_fc2) + b_fc2)

  # Defining correct prediction as a Tensor as well
  correct_prediction = tf.equal(tf.argmax(prediction,1), tf.argmax(v_ys,1))
  accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

def compute_accuracy(imageTestName , imageTestLabel ):
      global prediction
      v_xs = np.empty([1,224,224], dtype=np.int)
      g = imageTestName[0]
      v_xs = np.array(Image.open(path+g))
      v_xs = np.reshape(v_xs,[1,224,224])
      v_ys = imageTestLabel[0] ## It is a list = ['0', '0', '0', '0', '0', '0', '1', '0']
      v_ys = np.reshape(v_ys , [1,8]) ## After reshape it is = [['0' '0' '0' '0' '0' '0' '1' '0']]

      # Directly evaluate `accuracy`
      result = sess.run(accuracy, feed_dict={xs: v_xs, ys: v_ys, keep_prob: 1})
      return result