我知道有其他关于这个主题错误的帖子，但我不知道如何将它们应用到我的代码中。 我可能只是需要另一双眼睛来观察我的代码，以找到我的错误之处。 我为篇幅过长而道歉。
我正在做一个图像识别项目。
首先，我导入了库。
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from keras.models import Sequential
from keras.layers.core import Dense
from keras.optimizers import SGD
from keras.preprocessing.image import ImageDataGenerator
from keras.models import Sequential
from keras.layers.normalization import BatchNormalization
from keras.layers.convolutional import Conv2D
from keras.layers.convolutional import MaxPooling2D
from keras.layers.core import Activation
from keras.layers.core import Flatten, Dropout
from keras import backend as K
from imutils import paths
import matplotlib.pyplot as plt
import numpy as np
import argparse
import random
import pickle
import cv2
import os
接下来，我实例化我的CNN模型。
class SmallVGGNet:
@staticmethod
def build(width, height, depth, classes):
model = Sequential()
inputShape = (height, width, depth)
chanDim = -1
if K.image_data_format() == "channels_first":
inputShape = (depth, height, width)
chanDim = 1
# CONV => RELU => POOL layer set
model.add(Conv2D(32, (3, 3), padding="same",
input_shape=inputShape))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
# (CONV => RELU) * 2 => POOL layer set
model.add(Conv2D(64, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(Conv2D(64, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
# (CONV => RELU) * 3 => POOL layer set
model.add(Conv2D(128, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(Conv2D(128, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(Conv2D(128, (3, 3), padding="same"))
model.add(Activation("relu"))
model.add(BatchNormalization(axis=chanDim))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
# first (and only) set of FC => RELU layers
model.add(Flatten())
model.add(Dense(512))
model.add(Activation("relu"))
model.add(BatchNormalization())
model.add(Dropout(0.5))
# softmax classifier
model.add(Dense(classes))
model.add(Activation("softmax"))
# return the constructed network architecture
return model
收集并处理我的图像
data = []
labels = []
# grab the image paths and randomly shuffle them
shiba_path = sorted(list(paths.list_images('../downloads/shiba')))
fox_path = sorted(list(paths.list_images('../downloads/fox')))
combine_path = [shiba_path,fox_path]
random.seed(42)
# loop over the input data dictionaries
for path in combine_path:
path = random.shuffle(path)
# loop over the input images
for imagePaths in combine_path:
for imagePath in imagePaths:
try:
# load the image, resize the image to be 32x32 pixels (ignoring
# aspect ratio), flatten the image into 32x32x3=3072 pixel image
# into a list, and store the image in the data list
image = cv2.imread(imagePath)
image = cv2.resize(image, (64, 64))
data.append(image)
# extract the class label from the image path and update the
# labels list
label = imagePath.split(os.path.sep)[-2].split('/')[-1]
labels.append(label)
except:
pass
# scale the raw pixel intensities to the range [0, 1]
data = np.array(data, dtype="float") / 255.0
labels = np.array(labels)
运行我的模型
# partition the data into training and testing splits using 75% of
# the data for training and the remaining 25% for testing
(trainX, testX, trainY, testY) = train_test_split(data,
labels, test_size=0.25, random_state=42)
# convert the labels from integers to vectors
lb = LabelBinarizer()
trainY = lb.fit_transform(trainY)
testY = lb.transform(testY)
trainY 有一个形状为 (1429,1) 和 trainX 形状 (1429, 64, 64, 3)
# construct the image generator for data augmentation
aug = ImageDataGenerator(rotation_range=30, width_shift_range=0.1,
height_shift_range=0.1, shear_range=0.2, zoom_range=0.2,
horizontal_flip=True, fill_mode="nearest")
# initialize our VGG-like Convolutional Neural Network
model = SmallVGGNet.build(width=64, height=64, depth=3,
classes=len(lb.classes_))
问题出在下一步。
# initialize our initial learning rate, # of epochs to train for,
# and batch size
INIT_LR = 0.01
EPOCHS = 20
BS = 32
# initialize the model and optimizer
print("[INFO] training network...")
opt = SGD(lr=INIT_LR, decay=INIT_LR / EPOCHS)
model.compile(loss="categorical_crossentropy", optimizer=opt,
metrics=["accuracy"])
# train the network
H = model.fit_generator(aug.flow(trainX, trainY, batch_size=BS),
validation_data=(testX, testY), steps_per_epoch=len(trainX) // BS,
epochs=EPOCHS)
这一步会产生
检查目标时出错：预期 activation_8 的形状是(2,)，但得到的是形状为(1,)的数组。
我到底哪里出错了？ 我做错了什么，它希望我的Y的形状是2？ 它的形状应该是1还是2？
任何帮助将是非常感激的。
我能够重新创建错误，问题出在最后的激活层和损失函数中。
由于你已经将目标变量转换为One-Hot编码，使用了 LabelBinarizer你的目标数据看起来是这样的，其中包括两个类。
array([[1],
[1],
[1],
[0],
[0],
[1],
[0],
[0]])
在输出层中，不要把类的数量作为输入，而是给1，因为它只输出一个值，并且把损失函数从 categorical_crossentropy 到 binary_crossentropy
下面是修改后的区块。
# softmax classifier
model.add(Dense(1))
model.add(Activation("softmax"))
把损耗功能改成这样。
model.compile(loss="binary_crossentropy", optimizer=opt,
metrics=["accuracy"])
这样就可以解决你的问题了，学习愉快！
编辑1、提供模型摘要。
提供模型摘要
Model: "sequential_1"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
conv2d_1 (Conv2D) (None, 64, 64, 32) 896
_________________________________________________________________
activation_1 (Activation) (None, 64, 64, 32) 0
_________________________________________________________________
batch_normalization_1 (Batch (None, 64, 64, 32) 128
_________________________________________________________________
max_pooling2d_1 (MaxPooling2 (None, 32, 32, 32) 0
_________________________________________________________________
dropout_1 (Dropout) (None, 32, 32, 32) 0
_________________________________________________________________
conv2d_2 (Conv2D) (None, 32, 32, 64) 18496
_________________________________________________________________
activation_2 (Activation) (None, 32, 32, 64) 0
_________________________________________________________________
batch_normalization_2 (Batch (None, 32, 32, 64) 256
_________________________________________________________________
conv2d_3 (Conv2D) (None, 32, 32, 64) 36928
_________________________________________________________________
activation_3 (Activation) (None, 32, 32, 64) 0
_________________________________________________________________
batch_normalization_3 (Batch (None, 32, 32, 64) 256
_________________________________________________________________
max_pooling2d_2 (MaxPooling2 (None, 16, 16, 64) 0
_________________________________________________________________
dropout_2 (Dropout) (None, 16, 16, 64) 0
_________________________________________________________________
conv2d_4 (Conv2D) (None, 16, 16, 128) 73856
_________________________________________________________________
activation_4 (Activation) (None, 16, 16, 128) 0
_________________________________________________________________
batch_normalization_4 (Batch (None, 16, 16, 128) 512
_________________________________________________________________
conv2d_5 (Conv2D) (None, 16, 16, 128) 147584
_________________________________________________________________
activation_5 (Activation) (None, 16, 16, 128) 0
_________________________________________________________________
batch_normalization_5 (Batch (None, 16, 16, 128) 512
_________________________________________________________________
conv2d_6 (Conv2D) (None, 16, 16, 128) 147584
_________________________________________________________________
activation_6 (Activation) (None, 16, 16, 128) 0
_________________________________________________________________
batch_normalization_6 (Batch (None, 16, 16, 128) 512
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 8, 8, 128) 0
_________________________________________________________________
dropout_3 (Dropout) (None, 8, 8, 128) 0
_________________________________________________________________
flatten_1 (Flatten) (None, 8192) 0
_________________________________________________________________
dense_1 (Dense) (None, 512) 4194816
_________________________________________________________________
activation_7 (Activation) (None, 512) 0
_________________________________________________________________
batch_normalization_7 (Batch (None, 512) 2048
_________________________________________________________________
dropout_4 (Dropout) (None, 512) 0
_________________________________________________________________
dense_2 (Dense) (None, 1) 513
_________________________________________________________________
activation_8 (Activation) (None, 1) 0
=================================================================
Total params: 4,624,897
Trainable params: 4,622,785
Non-trainable params: 2,112