当我运行model.fit()时出现Tensorflow错误。这是我的代码。
train_data = pd.read_csv('train.csv')train_data = shuffle(train_data).reset_index(drop=True)split_data = np.array_split(train_data, 50)train_image = []for i in tqdm(range(split_data[0].shape[0])): path = 'train/train/'+str(train_data['category'][i]).zfill(2)+'/'+train_data['filename'][i] img = image.load_img(path,target_size=(400,400,3)) img = image.img_to_array(img) img = img/255 train_image.append(img)X = np.array(train_image) # X.shape (2108, 400, 400, 3)y = np.array(split_data[0]['category']) # y.shape (2108,)X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=42, test_size=0.1)这是我的CNN模型。
model = Sequential()model.add(Conv2D(filters=16, kernel_size=(5, 5), activation="relu", input_shape=(400,400,3)))model.add(MaxPooling2D(pool_size=(2, 2)))model.add(Dropout(0.25))...model.add(Flatten())model.add(Dense(128, activation='relu'))model.add(Dropout(0.5))model.add(Dense(42, activation='sigmoid'))model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy'])model.fit(X_train, y_train, epochs=10, validation_data=(X_test, y_test), batch_size=64)运行model.fit()时出现错误
ValueError: logits和labels必须具有相同的形状((None, 42) vs (None, 1))X_train的值
array([[[[0.99607843, 0.99607843, 0.99607843], [0.99607843, 0.99607843, 0.99607843], [0.99607843, 0.99607843, 0.99607843], ..., [1. , 1. , 1. ], [1. , 1. , 1. ], [1. , 1. , 1. ]], ..., ]]], dtype=float32)和y_train的值
array([ 5, 41, 24, ..., 41, 19, 40], dtype=int64)回答：
您正在进行多分类问题，您的标签也是整数编码的
使用softmax作为最后一层的激活函数：Dense(42, activation='softmax')
并使用sparse_categorical_crossentropy作为损失函数
