張量流不兼容形狀

Question

我是CNN的新手，我正在嘗試製作一個CNN來對手寫英文字母（az），（AZ）和數字（0-9）的圖像數據集進行分類，這些數據集有62個標籤。每個圖像大小爲30 * 30像素。我按照教程https://www.tensorflow.org/get_started/mnist/pros中的步驟操作。 當我運行模式，我得到一個錯誤

tensorflow.python.framework.errors.InvalidArgumentError: Incompatible shapes: [40] vs. [10]

我的批處理大小爲10，錯誤似乎是在correct_prediction。 在Tensorflow Incompatable Shapes Error in Tutorial發現相同的問題的解決方案，並沒有解決我的問題。任何幫助將不勝感激。數據集首先被壓縮，這是我的代碼。

import tensorflow as tf 
import pandas as pd 
import numpy as np 
from sklearn.model_selection import train_test_split 
X = [] 
y = [] 
import pickle 

#load data from pickle 
pickle_file = 'let.pickle' 

with open(pickle_file, 'rb') as f: 
    save = pickle.load(f) 
    X = save['dataset'] 
    y = save['labels'] 

    del save # hint to help gc free up memory 

#normalise the features 
X = (X - 255/2)/255 

# one hot encoding 
y = pd.get_dummies(y) 
y = y.values # change to ndarray 
y = np.float32(y) 
X = np.float32(X) 
Xtr, Xte, Ytr, Yte = train_test_split(X, y, train_size=0.7) 

batch_size = 10 

sess = tf.InteractiveSession() 

x = tf.placeholder(tf.float32, shape=[None, 900]) 
y_ = tf.placeholder(tf.float32, shape=[None, 62]) 

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): 
    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME') 

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

W_conv1 = weight_variable([5, 5, 1, 32]) 
b_conv1 = bias_variable([32]) 

x_image = tf.reshape(x, [-1,30,30,1]) 

h_conv1 = tf.nn.relu(conv2d(x_image, W_conv1) + b_conv1) 
h_pool1 = max_pool_2x2(h_conv1) 

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) 
h_pool2 = max_pool_2x2(h_conv2) 

W_fc1 = weight_variable([4 * 4 * 64, 1024]) 
b_fc1 = bias_variable([1024]) 

h_pool2_flat = tf.reshape(h_pool2, [-1, 4*4*64]) 
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) 

keep_prob = tf.placeholder(tf.float32) 
h_fc1_drop = tf.nn.dropout(h_fc1, keep_prob) 

W_fc2 = weight_variable([1024, 62]) 
b_fc2 = bias_variable([62]) 

y_conv = tf.matmul(h_fc1_drop, W_fc2) + b_fc2 

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) 
train_step = tf.train.AdamOptimizer(1e-4).minimize(cross_entropy) 
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) 

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32)) 
sess.run(tf.global_variables_initializer()) 
for i in range(20000): 
    offset = (i * batch_size) % (Ytr.shape[0] - batch_size) 
    batch_x = Xtr[offset:(offset + batch_size), :] 
    batch_y = Ytr[offset:(offset + batch_size), :] 
    if i%100 == 0: 
     train_accuracy = accuracy.eval(feed_dict={x:batch_x , y_: batch_y, keep_prob: 1.0}) 
     print("step %d, training accuracy %g"%(i, train_accuracy)) 
    train_step.run(feed_dict={x: Xtr[offset:(offset + batch_size), :], y_: Ytr[offset:(offset + batch_size), :], keep_prob: 0.5}) 

print("test accuracy %g"%accuracy.eval(feed_dict={x: Xte, y_: Yte, keep_prob: 1.0})) 

Answer 1

我改變輸入的尺寸以完全連接層4至8，現在它的工作

W_fc1 = weight_variable([8 * 8 * 64, 1024]) 
b_fc1 = bias_variable([1024]) 

#the input should be shaped/flattened 
h_pool2_flat = tf.reshape(h_pool2, [-1, 8*8*64]) 
h_fc1 = tf.nn.relu(tf.matmul(h_pool2_flat, W_fc1) + b_fc1) 

Answer 2

在這些行的代碼請看：

cross_entropy = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(labels=y_, logits=y_conv)) 
correct_prediction = tf.equal(tf.argmax(y_conv,1), tf.argmax(y_,1)) 

如果標籤是用正確的預測的索引（例如：[1,32,13, ...]）的列表的功能softmax_cross_entropy_with_logits是正確的。這意味着錯誤在這些行中。我加入了註釋，他們這樣做：

tf.argmax(y_conv,1) # Takes the max index of logits 
tf.argmax(y_,1) # Takes the max index of ???. 

雖然我沒有測試它，這條線替換它應該工作：

correct_prediction = tf.equal(tf.argmax(y_conv,1), y_) 

讓我知道你什麼時候修好了：d