python - Store images into multiply array and use it to train model

Question

I get this error when train the model:

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

I want to store my images into 33 array from folder using path. I have categories the images into different folder which were 1,2,3,4,5... I have use this code to do it but i dont know how to store it into different array. Can someone help me.

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()

After I save the file, then I use this code to train model and I want get 33 output layer but it only can work when my output layer(Dense) set 1. I got this error:

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

Here was my training code.

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)

标签： pythonopencvtensorflowkerasdeep-learning

Answer 1

您需要更改您的yas one hot 编码数据来进行培训。

试试这个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，如果您想分类 33 类而不是将损失更改为categorical_crossentropy.