Question

训练模型时出现此错误：

datadir = 'C:/Users/user/Desktop/RESIZE' #path of the folder
categories = ['1','2','3','4','5','6','7','8','9','A','B','C','D','E','F','G','H','I','K','L','M','N','O','P','Q','R','S','T','U','V','W','X','Y']
img_rows, img_cols = 100, 100
training_data = []
for category in categories:
    path = os.path.join(datadir,category)
    class_num = categories.index(category)
    for img in os.listdir(path):
        img_array = cv2.imread(os.path.join(path,img),cv2.IMREAD_GRAYSCALE)
        new_array = cv2.resize(img_array,(img_rows,img_cols))
        training_data.append([new_array,class_num])

random.shuffle(training_data)

X = []
y = []
for features, label in training_data:
    X.append(features)
    y.append(label)

X = np.array(X).reshape(-1,img_rows,img_cols,1)
X = X.astype("float32")
pickle_out = open("X.pickle","wb")
pickle.dump(X,pickle_out)
pickle_out.close()

pickle_out = open("y.pickle","wb")
pickle.dump(y,pickle_out)
pickle_out.close()

我想使用路径将图像从文件夹存储到33个数组中。我将图像分类到分别为1,2,3,4,5的不同文件夹中... 我已经使用此代码来执行此操作，但我不知道如何将其存储到其他数组中。有人可以帮我吗？

ValueError: Error when checking target: 
expected dropout_5 to have shape (33,) but got array with shape (1,)

保存文件后，我将使用此代码来训练模型，我想获得33个输出层，但仅当我的输出层（密集）设置为1时，它才能工作。我收到此错误：

import tensorflow as tf
from keras import optimizers
from keras.models import Sequential
from keras.layers import Dense
from keras.layers import Dropout
from keras.layers import Flatten
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers import Activation

import cv2
import os
import numpy as np
import pickle
from sklearn.utils import shuffle


X = pickle.load(open("X.pickle","rb"))
y = pickle.load(open("y.pickle","rb"))

X = X/255.0




model = Sequential()
model.add(Conv2D(32,(3,3), input_shape = X.shape[1:]))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(64,(3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.25))

model.add(Conv2D(128,(3,3)))
model.add(Activation("relu"))
model.add(MaxPooling2D(pool_size = (2,2)))
model.add(Dropout(0.4))

model.add(Dense(128))
model.add(Activation("relu"))
model.add(Dropout(0.5))

model.add(Flatten())
model.add(Dense(33, activation='softmax'))

model.add(Dropout(0.4))

model.compile(loss = "binary_crossentropy", optimizer = "adam", metrics = ["accuracy"])
model.fit(X, y, batch_size = 2, epochs = 1, validation_split = 0.2)

这是我的训练代码。

trunc

Answer 1

您需要将y更改为一种热门编码数据才能进行训练。

尝试y，

from sklearn.preprocessing import LabelEncoder
from sklearn.preprocessing import OneHotEncoder

# integer encode
label_encoder = LabelEncoder()
integer_encoded = label_encoder.fit_transform(y)
print(label_encoder.classes_) # This is your classes.
print(integer_encoded.shape())
# binary encode
onehot_encoder = OneHotEncoder(sparse=False)
integer_encoded = integer_encoded.reshape(len(integer_encoded), 1)
onehot_encoded = onehot_encoder.fit_transform(integer_encoded)
print(onehot_encoded.shape())

还有一件事情，如果您要分类为33类，而不是将损失更改为categorical_crossentropy。

将图像存储到乘法数组中，并用于训练模型

1 个答案: