软件包版本:
- Tensorflow = = 2.5 Python 3.8 = =
- Keras = = 2.3
代码如下:
# Pipe Line
(x_train, y_train), (x_test, y_test), (x_val, y_val) = (X_train, Y_train), (X_test, Y_test), (X_val, Y_val)
def model_seg():
# Convolution Layers (BatchNorm after non-linear activation)
img_input = Input(shape= (192, 256, 3))
x = Conv2D(16, (3, 3), padding='same', name='conv1')(img_input)
x = BatchNormalization(name='bn1')(x)
x = Activation('relu')(x)
x = Conv2D(32, (3, 3), padding='same', name='conv2')(x)
x = BatchNormalization(name='bn2')(x)
x = Activation('relu')(x)
x = MaxPooling2D()(x)
x = Conv2D(64, (3, 3), padding='same', name='conv3')(x)
x = BatchNormalization(name='bn3')(x)
x = Activation('relu')(x)
x = Conv2D(64, (3, 3), padding='same', name='conv4')(x)
x = BatchNormalization(name='bn4')(x)
x = Activation('relu')(x)
x = MaxPooling2D()(x)
x = Conv2D(128, (3, 3), padding='same', name='conv5')(x)
x = BatchNormalization(name='bn5')(x)
x = Activation('relu')(x)
x = Conv2D(128, (4, 4), padding='same', name='conv6')(x)
x = BatchNormalization(name='bn6')(x)
x = Activation('relu')(x)
x = MaxPooling2D()(x)
x = Conv2D(256, (3, 3), padding='same', name='conv7')(x)
x = BatchNormalization(name='bn7')(x)
x = Dropout(0.5)(x)
x = Activation('relu')(x)
x = Conv2D(256, (3, 3), padding='same', name='conv8')(x)
x = BatchNormalization(name='bn8')(x)
x = Activation('relu')(x)
x = MaxPooling2D()(x)
x = Conv2D(512, (3, 3), padding='same', name='conv9')(x)
x = BatchNormalization(name='bn9')(x)
x = Activation('relu')(x)
x = Dense(1024, activation = 'relu', name='fc1')(x)
x = Dense(1024, activation = 'relu', name='fc2')(x)
# Deconvolution Layers (BatchNorm after non-linear activation)
x = Conv2DTranspose(256, (3, 3), padding='same', name='deconv1')(x)
x = BatchNormalization(name='bn19')(x)
x = Activation('relu')(x)
x = UpSampling2D()(x)
x = Conv2DTranspose(256, (3, 3), padding='same', name='deconv2')(x)
x = BatchNormalization(name='bn12')(x)
x = Activation('relu')(x)
x = Conv2DTranspose(128, (3, 3), padding='same', name='deconv3')(x)
x = BatchNormalization(name='bn13')(x)
x = Activation('relu')(x)
x = UpSampling2D()(x)
x = Conv2DTranspose(128, (4, 4), padding='same', name='deconv4')(x)
x = BatchNormalization(name='bn14')(x)
x = Activation('relu')(x)
x = Conv2DTranspose(128, (3, 3), padding='same', name='deconv5')(x)
x = BatchNormalization(name='bn15')(x)
x = Activation('relu')(x)
x = UpSampling2D()(x)
x = Conv2DTranspose(64, (3, 3), padding='same', name='deconv6')(x)
x = BatchNormalization(name='bn16')(x)
x = Activation('relu')(x)
x = Conv2DTranspose(32, (3, 3), padding='same', name='deconv7')(x)
x = BatchNormalization(name='bn20')(x)
x = Activation('relu')(x)
x = UpSampling2D()(x)
x = Conv2DTranspose(16, (3, 3), padding='same', name='deconv8')(x)
x = BatchNormalization(name='bn17')(x)
x = Dropout(0.5)(x)
x = Activation('relu')(x)
x = Conv2DTranspose(1, (3, 3), padding='same', name='deconv9')(x)
x = BatchNormalization(name='bn18')(x)
x = Activation('sigmoid')(x)
pred = Reshape((192,256))(x)
model = Model(inputs=img_input, outputs=pred)
model.compile(optimizer= Adam(lr = 0.003), loss= [jaccard_distance], metrics=[iou])
hist = model.fit(x_train, y_train, epochs= 300, batch_size= 16,validation_data=(x_test, y_test), verbose=1)
model.save("model.h5")
accuracy = model.evaluate(x=x_test,y=y_test,batch_size=16)
print("Accuracy: ",accuracy[1])
在类型转换中给我这个错误，我不知道如何修复它:
return gen_math_ops.mul(x, y, name)
D:road-damageroad-damage-detectionrddlibsite-packagestensorflowpythonopsgen_math_ops.py:6248 mul
_, _, _op, _outputs = _op_def_library._apply_op_helper(
D:road-damageroad-damage-detectionrddlibsite-packagestensorflowpythonframeworkop_def_library.py:555 _apply_op_helper
raise TypeError(
TypeError: Input 'y' of 'Mul' Op has type float32 that does not match type uint8 of argument 'x'.
在你代码的某个地方，有一个是浮点数的张量和一个是整数的张量，它不确定结果应该是哪一个。
你的网络架构并没有告诉我们太多;这很可能与您准备数据的方式有关。
如果可以将X和Y视为浮点数，那么在将它们传递给fit之前，请尝试像这样显式地将它们转换为浮点数:
x_train = x_train.astype(np.float)
x_test = x_test.astype(np.float)
y_train = y_train.astype(np.float)
y_test = y_test.astype(np.float)