无监督卷积自动编码器总是给出空白输出
Unsupervised Convolutional Autoencoder is always giving blank output
我正在尝试使用无监督图像训练自动编码器。我有大约 300 个训练图像和 100 个验证图像。但是当我将一个看不见的图像输入到经过训练的自动编码器时，它给出了完全空白的输出。
train_images = os.listdir('./Data/train')
val_images = os.listdir('./Data/val')
X_train = []
X_val = []
for i in range(len(train_images)):
img = cv2.imread('./Data/train/'+train_images[i])
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
resized = cv2.resize(img, (224,224), interpolation = cv2.INTER_AREA)
X_train.append(resized)
X_train = np.asarray(X_train)
X_train = X_train.astype('float32')/255.
X_train = np.reshape(X_train, (len(X_train), 224, 224, 1))
for i in range(len(val_images)):
img = cv2.imread('./Data/val/'+val_images[i])
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
resized = cv2.resize(img, (224,224), interpolation = cv2.INTER_AREA)
X_val.append(resized)
X_val = np.asarray(X_val)
X_val = X_val.astype('float32')/255.
X_val = np.reshape(X_val, (len(X_val), 224, 224, 1))
print(len(X_train))
print(len(X_val))
这里X_train.shape和X_val.shape分别是(300,224,224,1)和(100, 224, 224, 1)
这是我的 upconv_concat 函数
def upconv_concat(bottom_a, bottom_b, n_filter, pool_size, stride, padding='VALID'):
up_conv = Conv2DTranspose(filters=n_filter, kernel_size=[pool_size, pool_size],
strides=stride, padding=padding)(bottom_a)
return Concatenate(axis=-1)([up_conv, bottom_b])
这里有一些参数
input_img = Input(shape=(224, 224, 1))
droprate=0.25
num_classes = 1
这是我的模型
conv_1_1 = Conv2D(filters = 64, kernel_size = 3, activation='relu', padding='same')(input_img)
conv_1_1_bn = BatchNormalization()(conv_1_1)
conv_1_1_do = Dropout(droprate)(conv_1_1_bn)
pool_1 = MaxPooling2D(pool_size= 2, strides = 2)(conv_1_1_do)
conv_4_1 = SeparableConv2D(filters = 512, kernel_size = 3, activation='relu', padding='same')(pool_1)
conv_4_1_bn = BatchNormalization()(conv_4_1)
conv_4_1_do = Dropout(droprate)(conv_4_1_bn)
pool_4 = MaxPooling2D(pool_size= 2, strides = 2)(conv_4_1_do)
conv_5_1 = SeparableConv2D(filters = 1024, kernel_size = 3, activation='relu', padding='same')(pool_4)
conv_5_1_bn = BatchNormalization()(conv_5_1)
conv_5_1_do = Dropout(droprate)(conv_5_1_bn)
upconv_1 = upconv_concat(conv_5_1_do, conv_4_1_do, n_filter=512, pool_size=2, stride=2)
conv_6_1 = SeparableConv2D(filters = 512, kernel_size = 3, activation='relu', padding='same')(upconv_1)
conv_6_1_bn = BatchNormalization()(conv_6_1)
conv_6_1_do = Dropout(droprate)(conv_6_1_bn)
upconv_2 = upconv_concat(conv_6_1_do, conv_1_1_do, n_filter=64, pool_size=2, stride=2)
conv_9_1 = SeparableConv2D(filters = 64, kernel_size = 3, activation='relu', padding='same')(upconv_2)
conv_9_1_bn = BatchNormalization()(conv_9_1)
conv_9_1_do = Dropout(droprate)(conv_9_1_bn)
ae_output = Conv2D(num_classes, kernel_size=1, strides = (1,1), activation="softmax")(conv_9_1_do)
这里是训练部分
ae_model = Model(input_img, ae_output)
ae_model.compile(optimizer='adadelta', loss='binary_crossentropy')
ae_model.fit(X_train, X_train,
epochs=5,
batch_size=16,
shuffle=True,
validation_data=(X_val, X_val))
如果有人需要模型摘要
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 224, 224, 1) 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 224, 224, 64) 640 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 224, 224, 64) 256 conv2d_1[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 224, 224, 64) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 112, 112, 64) 0 dropout_1[0][0]
__________________________________________________________________________________________________
separable_conv2d_1 (SeparableCo (None, 112, 112, 512 33856 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 112, 112, 512 2048 separable_conv2d_1[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 112, 112, 512 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 56, 56, 512) 0 dropout_2[0][0]
__________________________________________________________________________________________________
separable_conv2d_2 (SeparableCo (None, 56, 56, 1024) 529920 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 56, 56, 1024) 4096 separable_conv2d_2[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout) (None, 56, 56, 1024) 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_transpose_1 (Conv2DTrans (None, 112, 112, 512 2097664 dropout_3[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 112, 112, 102 0 conv2d_transpose_1[0][0]
dropout_2[0][0]
__________________________________________________________________________________________________
separable_conv2d_3 (SeparableCo (None, 112, 112, 512 534016 concatenate_1[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 112, 112, 512 2048 separable_conv2d_3[0][0]
__________________________________________________________________________________________________
dropout_4 (Dropout) (None, 112, 112, 512 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_transpose_2 (Conv2DTrans (None, 224, 224, 64) 131136 dropout_4[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 224, 224, 128 0 conv2d_transpose_2[0][0]
dropout_1[0][0]
__________________________________________________________________________________________________
separable_conv2d_4 (SeparableCo (None, 224, 224, 64) 9408 concatenate_2[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 224, 224, 64) 256 separable_conv2d_4[0][0]
__________________________________________________________________________________________________
dropout_5 (Dropout) (None, 224, 224, 64) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 224, 224, 1) 65 dropout_5[0][0]
==================================================================================================
Total params: 3,345,409
Trainable params: 3,341,057
Non-trainable params: 4,352
__________________________________________________________________________________________________
我已经完全检查了 X_train 图像，看看我是否错误地发送了空白图像。但不是。我只发送正确的数据。
问题是
当我尝试测试模型时，它给出了一张空白图像。
img = cv2.imread('./test/a184.jpg')
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
resized = cv2.resize(img, (224,224), interpolation = cv2.INTER_AREA)
resized = resized/255
resized = resized[:, :, np.newaxis]
resized = resized[np.newaxis, :, :]
现在 resized 的形状是 (1,224,224,1)
image = ae_model.predict(resized)
image = image.reshape((224,224))
plt.imshow(image, cmap= 'gray')
给我这张图片：
但是 image 变量中的值都是 1s.
我正在使用 tf.Keras。
请帮我解决这个问题。无法定位问题出在哪里，如何调试才能找到。
黑色图像来自您激活最后一个转换层的方式。
这里你想为每个像素预测一个介于 0 和 1 之间的值，所以你需要一个 Sigmoid 激活函数，而不是 Softmax 激活函数。
试试这个：
ae_output = Conv2D(num_classes, kernel_size=1, strides = (1,1), activation="sigmoid")(conv_9_1_do)
我正在尝试使用无监督图像训练自动编码器。我有大约 300 个训练图像和 100 个验证图像。但是当我将一个看不见的图像输入到经过训练的自动编码器时，它给出了完全空白的输出。
train_images = os.listdir('./Data/train')
val_images = os.listdir('./Data/val')
X_train = []
X_val = []
for i in range(len(train_images)):
img = cv2.imread('./Data/train/'+train_images[i])
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
resized = cv2.resize(img, (224,224), interpolation = cv2.INTER_AREA)
X_train.append(resized)
X_train = np.asarray(X_train)
X_train = X_train.astype('float32')/255.
X_train = np.reshape(X_train, (len(X_train), 224, 224, 1))
for i in range(len(val_images)):
img = cv2.imread('./Data/val/'+val_images[i])
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
resized = cv2.resize(img, (224,224), interpolation = cv2.INTER_AREA)
X_val.append(resized)
X_val = np.asarray(X_val)
X_val = X_val.astype('float32')/255.
X_val = np.reshape(X_val, (len(X_val), 224, 224, 1))
print(len(X_train))
print(len(X_val))
这里X_train.shape和X_val.shape分别是(300,224,224,1)和(100, 224, 224, 1)
这是我的 upconv_concat 函数
def upconv_concat(bottom_a, bottom_b, n_filter, pool_size, stride, padding='VALID'):
up_conv = Conv2DTranspose(filters=n_filter, kernel_size=[pool_size, pool_size],
strides=stride, padding=padding)(bottom_a)
return Concatenate(axis=-1)([up_conv, bottom_b])
这里有一些参数
input_img = Input(shape=(224, 224, 1))
droprate=0.25
num_classes = 1
这是我的模型
conv_1_1 = Conv2D(filters = 64, kernel_size = 3, activation='relu', padding='same')(input_img)
conv_1_1_bn = BatchNormalization()(conv_1_1)
conv_1_1_do = Dropout(droprate)(conv_1_1_bn)
pool_1 = MaxPooling2D(pool_size= 2, strides = 2)(conv_1_1_do)
conv_4_1 = SeparableConv2D(filters = 512, kernel_size = 3, activation='relu', padding='same')(pool_1)
conv_4_1_bn = BatchNormalization()(conv_4_1)
conv_4_1_do = Dropout(droprate)(conv_4_1_bn)
pool_4 = MaxPooling2D(pool_size= 2, strides = 2)(conv_4_1_do)
conv_5_1 = SeparableConv2D(filters = 1024, kernel_size = 3, activation='relu', padding='same')(pool_4)
conv_5_1_bn = BatchNormalization()(conv_5_1)
conv_5_1_do = Dropout(droprate)(conv_5_1_bn)
upconv_1 = upconv_concat(conv_5_1_do, conv_4_1_do, n_filter=512, pool_size=2, stride=2)
conv_6_1 = SeparableConv2D(filters = 512, kernel_size = 3, activation='relu', padding='same')(upconv_1)
conv_6_1_bn = BatchNormalization()(conv_6_1)
conv_6_1_do = Dropout(droprate)(conv_6_1_bn)
upconv_2 = upconv_concat(conv_6_1_do, conv_1_1_do, n_filter=64, pool_size=2, stride=2)
conv_9_1 = SeparableConv2D(filters = 64, kernel_size = 3, activation='relu', padding='same')(upconv_2)
conv_9_1_bn = BatchNormalization()(conv_9_1)
conv_9_1_do = Dropout(droprate)(conv_9_1_bn)
ae_output = Conv2D(num_classes, kernel_size=1, strides = (1,1), activation="softmax")(conv_9_1_do)
这里是训练部分
ae_model = Model(input_img, ae_output)
ae_model.compile(optimizer='adadelta', loss='binary_crossentropy')
ae_model.fit(X_train, X_train,
epochs=5,
batch_size=16,
shuffle=True,
validation_data=(X_val, X_val))
如果有人需要模型摘要
__________________________________________________________________________________________________
Layer (type) Output Shape Param # Connected to
==================================================================================================
input_1 (InputLayer) (None, 224, 224, 1) 0
__________________________________________________________________________________________________
conv2d_1 (Conv2D) (None, 224, 224, 64) 640 input_1[0][0]
__________________________________________________________________________________________________
batch_normalization_1 (BatchNor (None, 224, 224, 64) 256 conv2d_1[0][0]
__________________________________________________________________________________________________
dropout_1 (Dropout) (None, 224, 224, 64) 0 batch_normalization_1[0][0]
__________________________________________________________________________________________________
max_pooling2d_1 (MaxPooling2D) (None, 112, 112, 64) 0 dropout_1[0][0]
__________________________________________________________________________________________________
separable_conv2d_1 (SeparableCo (None, 112, 112, 512 33856 max_pooling2d_1[0][0]
__________________________________________________________________________________________________
batch_normalization_2 (BatchNor (None, 112, 112, 512 2048 separable_conv2d_1[0][0]
__________________________________________________________________________________________________
dropout_2 (Dropout) (None, 112, 112, 512 0 batch_normalization_2[0][0]
__________________________________________________________________________________________________
max_pooling2d_2 (MaxPooling2D) (None, 56, 56, 512) 0 dropout_2[0][0]
__________________________________________________________________________________________________
separable_conv2d_2 (SeparableCo (None, 56, 56, 1024) 529920 max_pooling2d_2[0][0]
__________________________________________________________________________________________________
batch_normalization_3 (BatchNor (None, 56, 56, 1024) 4096 separable_conv2d_2[0][0]
__________________________________________________________________________________________________
dropout_3 (Dropout) (None, 56, 56, 1024) 0 batch_normalization_3[0][0]
__________________________________________________________________________________________________
conv2d_transpose_1 (Conv2DTrans (None, 112, 112, 512 2097664 dropout_3[0][0]
__________________________________________________________________________________________________
concatenate_1 (Concatenate) (None, 112, 112, 102 0 conv2d_transpose_1[0][0]
dropout_2[0][0]
__________________________________________________________________________________________________
separable_conv2d_3 (SeparableCo (None, 112, 112, 512 534016 concatenate_1[0][0]
__________________________________________________________________________________________________
batch_normalization_4 (BatchNor (None, 112, 112, 512 2048 separable_conv2d_3[0][0]
__________________________________________________________________________________________________
dropout_4 (Dropout) (None, 112, 112, 512 0 batch_normalization_4[0][0]
__________________________________________________________________________________________________
conv2d_transpose_2 (Conv2DTrans (None, 224, 224, 64) 131136 dropout_4[0][0]
__________________________________________________________________________________________________
concatenate_2 (Concatenate) (None, 224, 224, 128 0 conv2d_transpose_2[0][0]
dropout_1[0][0]
__________________________________________________________________________________________________
separable_conv2d_4 (SeparableCo (None, 224, 224, 64) 9408 concatenate_2[0][0]
__________________________________________________________________________________________________
batch_normalization_5 (BatchNor (None, 224, 224, 64) 256 separable_conv2d_4[0][0]
__________________________________________________________________________________________________
dropout_5 (Dropout) (None, 224, 224, 64) 0 batch_normalization_5[0][0]
__________________________________________________________________________________________________
conv2d_2 (Conv2D) (None, 224, 224, 1) 65 dropout_5[0][0]
==================================================================================================
Total params: 3,345,409
Trainable params: 3,341,057
Non-trainable params: 4,352
__________________________________________________________________________________________________
我已经完全检查了 X_train 图像，看看我是否错误地发送了空白图像。但不是。我只发送正确的数据。
问题是
当我尝试测试模型时，它给出了一张空白图像。
img = cv2.imread('./test/a184.jpg')
img = cv2.cvtColor(img,cv2.COLOR_BGR2GRAY)
resized = cv2.resize(img, (224,224), interpolation = cv2.INTER_AREA)
resized = resized/255
resized = resized[:, :, np.newaxis]
resized = resized[np.newaxis, :, :]
现在 resized 的形状是 (1,224,224,1)
image = ae_model.predict(resized)
image = image.reshape((224,224))
plt.imshow(image, cmap= 'gray')
给我这张图片：
image 变量中的值都是 1s.
我正在使用 tf.Keras。
请帮我解决这个问题。无法定位问题出在哪里，如何调试才能找到。
黑色图像来自您激活最后一个转换层的方式。
这里你想为每个像素预测一个介于 0 和 1 之间的值，所以你需要一个 Sigmoid 激活函数，而不是 Softmax 激活函数。
试试这个：
ae_output = Conv2D(num_classes, kernel_size=1, strides = (1,1), activation="sigmoid")(conv_9_1_do)