input depth must be evenly divisible by filter depth: 1 vs 3
时间: 2024-05-24 19:10:42 浏览: 19
This error occurs when you try to access the `trainable_variables` attribute of the TensorFlow module, but it does not exist.
This attribute is used to get all the trainable variables in a TensorFlow graph. It is commonly used in machine learning models to get the weights and biases of the neural network layers for training and optimization.
To fix this error, make sure you have imported the `tensorflow` module correctly and that it is up-to-date. Also, check that you are trying to access the attribute on the correct object, such as a TensorFlow model or optimizer.
相关问题
Node: 'model/conv2d/Relu' input depth must be evenly divisible by filter depth: 1 vs 4 [[{{node model/conv2d/Relu}}]] [Op:__inference_train_function_6066]
这个错误提示是在训练模型时出现的。它表示卷积层的输入深度必须能够被过滤器深度整除,但是这里输入深度是1,而过滤器深度是4,因此无法整除。这个错误通常是由模型架构中的错误导致的,可能是输入数据的形状与模型期望的形状不匹配,或者是某个层的参数设置有误。您需要检查模型的架构,确保每个层的输入和输出维度都正确,并且与数据的形状匹配。还可以尝试使用不同的过滤器大小、步幅、填充等参数,或者调整数据的形状以适应模型。
以下代码出现input depth must be evenly divisible by filter depth: 1 vs 3错误是为什么,代码应该怎么改import tensorflow as tf from keras.models import Sequential from keras.layers import Dense, Dropout, Flatten from keras.layers import Conv2D, MaxPooling2D from keras.optimizers import SGD from keras.utils import np_utils from keras.preprocessing.image import ImageDataGenerator from keras.applications.vgg16 import VGG16 import numpy # 加载FER2013数据集 with open('E:/BaiduNetdiskDownload/fer2013.csv') as f: content = f.readlines() lines = numpy.array(content) num_of_instances = lines.size print("Number of instances: ", num_of_instances) # 定义X和Y X_train, y_train, X_test, y_test = [], [], [], [] # 按行分割数据 for i in range(1, num_of_instances): try: emotion, img, usage = lines[i].split(",") val = img.split(" ") pixels = numpy.array(val, 'float32') emotion = np_utils.to_categorical(emotion, 7) if 'Training' in usage: X_train.append(pixels) y_train.append(emotion) elif 'PublicTest' in usage: X_test.append(pixels) y_test.append(emotion) finally: print("", end="") # 转换成numpy数组 X_train = numpy.array(X_train, 'float32') y_train = numpy.array(y_train, 'float32') X_test = numpy.array(X_test, 'float32') y_test = numpy.array(y_test, 'float32') # 数据预处理 X_train /= 255 X_test /= 255 X_train = X_train.reshape(X_train.shape[0], 48, 48, 1) X_test = X_test.reshape(X_test.shape[0], 48, 48, 1) # 定义VGG16模型 vgg16_model = VGG16(weights='imagenet', include_top=False, input_shape=(48, 48, 3)) # 微调模型 model = Sequential() model.add(vgg16_model) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(7, activation='softmax')) for layer in model.layers[:1]: layer.trainable = False # 定义优化器和损失函数 sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy']) # 数据增强 datagen = ImageDataGenerator( featurewise_center=False, featurewise_std_normalization=False, rotation_range=20, width_shift_range=0.2, height_shift_range=0.2, horizontal_flip=True) datagen.fit(X_train) # 训练模型 model.fit_generator(datagen.flow(X_train, y_train, batch_size=32), steps_per_epoch=len(X_train) / 32, epochs=10) # 评估模型 score = model.evaluate(X_test, y_test, batch_size=32) print("Test Loss:", score[0]) print("Test Accuracy:", score[1])
这个错误是由于卷积层(Conv2D)中的过滤器深度与输入张量深度不匹配导致的。在这个例子中,过滤器深度(filter depth)是3,但是输入张量(input depth)的深度是1,无法整除,因此出现错误。为了解决这个问题,我们需要用一个输入深度能够被过滤器深度整除的模型输入数据。
代码改动可以通过如下方式完成:
1. 修改模型输入数据的维度,以匹配过滤器深度的要求。
2. 转换输入数据的颜色通道,以生成一个3个通道的输入张量。
下面是一个修改后的代码示例:
import tensorflow as tf
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.optimizers import SGD
from keras.utils import np_utils
from keras.preprocessing.image import ImageDataGenerator
from keras.applications.vgg16 import VGG16
import numpy as np
# 修改输入数据的维度
img_rows, img_cols = 32, 32
input_shape = (img_rows, img_cols, 3)
# 载入数据集
(x_train, y_train), (x_test, y_test) = tf.keras.datasets.cifar10.load_data()
# 将数据转换为浮点数类型
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
# 将像素值归一化到[0, 1]
x_train /= 255
x_test /= 255
# 将类向量转换为二进制类矩阵
num_classes = 10
y_train = np_utils.to_categorical(y_train, num_classes)
y_test = np_utils.to_categorical(y_test, num_classes)
# 生成并优化模型
model = Sequential()
model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape))
model.add(Conv2D(32, (3, 3), activation='relu'))
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(num_classes, activation='softmax'))
sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(loss='categorical_crossentropy',
optimizer=sgd,
metrics=['accuracy'])
# 在训练数据上生成扩增的数据
batch_size = 100
epochs = 5
datagen = ImageDataGenerator(
featurewise_center=False, # 将输入数据集按均值去中心化
samplewise_center=False, # 将每个样本按均值去中心化
featurewise_std_normalization=False, # 将输入数据除以数据集的标准差
samplewise_std_normalization=False, # 将每个样本除以自身的标准差
zca_whitening=False, # ZCA白化
rotation_range=0, # 随机旋转图像范围
width_shift_range=0.1, # 随机水平移动图像范围
height_shift_range=0.1, # 随机垂直移动图像范围
horizontal_flip=True, # 随机翻转图像
vertical_flip=False # # 随机翻转图像
)
datagen.fit(x_train)
model.fit(datagen.flow(x_train, y_train,
batch_size=batch_size),
epochs=epochs,
validation_data=(x_test, y_test),
steps_per_epoch=x_train.shape[0] // batch_size)
# 输出模型的准确率
scores = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', scores[0])
print('Test accuracy:', scores[1])
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