我目前正在使用Cifar10数据集进行一点CNN工作。我只是在这里和那里更新了我的代码，现在它不起作用了。我想不通这个错误。预测告诉我"；而不是数字"；。我的问题找不到答案。所以我不能在不添加更多文本的情况下发布这个问题。我知道我应该在这里写些什么。现在好好吃顿早餐就好了。咖啡和煎饼之类的。我希望我现在可以提出这个问题。
from keras.datasets import cifar10
import numpy as np
(x_training, y_training), (x_test,y_test) = cifar10.load_data()
x_training = x_training / 255.0
x_test = x_test / 255.0
%matplotlib inline
import matplotlib.pyplot as plt
plt.imshow(x_training[3])
plt.show
from keras.models import Sequential
from keras.layers import Dense, Flatten,Conv2D , MaxPooling2D, Dropout
import tensorflow as tf
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), input_shape=(32, 32, 3), activation="relu", padding="same"))
model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", padding="same"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=(3, 3), activation="relu", padding="same"))
model.add(Conv2D(64, kernel_size=(3, 3), activation="relu", padding="same"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=(3, 3), activation="relu", padding="same"))
model.add(Conv2D(128, kernel_size=(3, 3), activation="relu", padding="same"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation="relu"))
model.add(Dense(128, activation="relu"))
model.add(Dense(1, activation="sigmoid"))
model.compile(optimizer='RMSProp', loss="binary_crossentropy", metrics=['accuracy'])
model.summary()
model.fit(x_training, y_training,batch_size=128, epochs=10, shuffle = True )
model.evaluate(x_training, y_training)
results = model.predict(x_training[1].reshape(-1, 32, 32, 3))
results
class_names = ['airplane', 'automobile', 'bird', 'cat', 'deer',
'dog', 'frog', 'horse', 'ship', 'truck']
max = np.max(results)
max_position = np.argmax(results)
class_name_predict = class_names[max_position]
plt.imshow(x_training[1])
plt.show
test = class_name_predict
test
plt.imshow(x_training[1])
plt.show
x = class_names[y_training[1][0]]
x
您的模型中存在一些错误：
多类问题的输出层的维数必须等于具有softmax激活函数的类的数量
多类问题的标准损失是categorical_crossentropy和sparse_categoricalcrossentropy。当你的目标是一个热编码标签时，可以使用categorical_crossentropy，当你有整数编码标签时(这是你的情况(使用sparse_categoricalcrossentropy
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3), input_shape=(32, 32, 3), activation="relu", padding="same"))
model.add(Conv2D(32, kernel_size=(3, 3), activation="relu", padding="same"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(64, kernel_size=(3, 3), activation="relu", padding="same"))
model.add(Conv2D(64, kernel_size=(3, 3), activation="relu", padding="same"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Conv2D(128, kernel_size=(3, 3), activation="relu", padding="same"))
model.add(Conv2D(128, kernel_size=(3, 3), activation="relu", padding="same"))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(256, activation="relu"))
model.add(Dense(128, activation="relu"))
model.add(Dense(len(class_names), activation="softmax"))
model.compile(optimizer='RMSProp', loss="sparse_categorical_crossentropy", metrics=['accuracy'])
model.summary()
这是正在运行的笔记本