如何输出模型def MEAN_Spot(opt): inputs1 = layers.Input(shape=(42, 42, 1)) inputs2 = layers.Input(shape=(42, 42, 1)) inputs3 = layers.Input(shape=(42, 42, 1)) inputs = layers.Concatenate()([inputs1, inputs2, inputs3]) conv1 = layers.Conv2D(8, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs) ba1 = BasicBlock(8, 18)(conv1) ba2 = BasicBlock(18, 28, stride=2)(ba1) att = BasicBlock1(28, 8, stride=2)(ba2) # interpretation 1 merged_conv = layers.Conv2D(8, (5,5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(att) merged_pool = layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2,2))(merged_conv) flat = layers.Flatten()(merged_pool) flat_do = layers.Dropout(0.2)(flat) # outputs outputs = layers.Dense(1, activation='linear', name='spot')(flat_do) #Takes input u, v, os model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs]) model.compile( loss={'spot':'mse'}, optimizer=opt, metrics={'spot':tf.keras.metrics.MeanAbsoluteError()}, ) return model
时间: 2023-09-18 10:13:07 浏览: 50
您可以使用 `model.summary()` 来打印输出模型的结构和参数数量等信息。这将输出模型的每一层的输出形状,参数数量以及总参数数量等信息。您可以在 `MEAN_Spot` 函数的最后添加以下代码来打印输出模型的信息:
```
model.summary()
```
这将打印输出模型的详细信息,包括每一层的名称、输出形状、参数数量等。您还可以使用 `keras.utils.plot_model()` 函数来可视化模型的结构。例如,以下代码将生成一个模型结构图:
```
keras.utils.plot_model(model, to_file='model.png', show_shapes=True)
```
其中,参数 `to_file` 指定图像的文件名,`show_shapes` 参数指定是否显示每一层的输出形状。
相关问题
def MEAN_Spot(opt): # channel 1 inputs1 = layers.Input(shape=(42, 42, 1)) inputs2 = layers.Input(shape=(42, 42, 1)) inputs3 = layers.Input(shape=(42, 42, 1)) # merge 1 inputs = layers.Concatenate()([inputs1, inputs2, inputs3]) conv1 = layers.Conv2D(3, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs)改为class定义形式
你可以将这段代码转化为一个类的形式,例如:
```
from tensorflow.keras import layers
from tensorflow.keras.regularizers import l2
class MeanSpot:
def __init__(self):
self.inputs1 = layers.Input(shape=(42, 42, 1))
self.inputs2 = layers.Input(shape=(42, 42, 1))
self.inputs3 = layers.Input(shape=(42, 42, 1))
def build(self):
# merge 1
inputs = layers.Concatenate()([self.inputs1, self.inputs2, self.inputs3])
conv1 = layers.Conv2D(3, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs)
return conv1
```
这样,你就可以使用 `MeanSpot` 类来创建一个 `MEAN_Spot` 对象,然后调用 `build()` 方法来构建模型。例如:
```
mean_spot = MeanSpot()
model = mean_spot.build()
```
这个模型与原来的 `MEAN_Spot` 函数是等价的。
class定义basic block(self, in_channels, out_channels, stride=1)模块之后如何放到def MEAN_Spot(opt): # channel 1 inputs1 = layers.Input(shape=(42, 42, 1)) inputs2 = layers.Input(shape=(42, 42, 1)) inputs3 = layers.Input(shape=(42, 42, 1)) # merge 1 inputs = layers.Concatenate()([inputs1, inputs2, inputs3]) conv1 = layers.Conv2D(3, (7,7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs)后面
可以在`def MEAN_Spot(opt)`中直接调用定义好的`BasicBlock`类,具体方法是在`def MEAN_Spot(opt)`中实例化`BasicBlock`类,然后将实例化的对象作为一个层连接到`conv1`之后。具体代码实现如下:
```python
class BasicBlock(keras.layers.Layer):
def __init__(self, out_channels, kernel_size=3, strides=1):
super(BasicBlock, self).__init__()
self.conv1 = keras.layers.Conv2D(out_channels, kernel_size, strides=strides, padding='same')
self.bn1 = keras.layers.BatchNormalization()
self.relu = keras.layers.ReLU()
self.conv2 = keras.layers.Conv2D(out_channels, kernel_size, strides=1, padding='same')
self.bn2 = keras.layers.BatchNormalization()
if strides != 1:
self.downsample = keras.Sequential([
keras.layers.Conv2D(out_channels, 1, strides=strides),
keras.layers.BatchNormalization()
])
else:
self.downsample = lambda x: x
def call(self, inputs, training=False):
identity = inputs
x = self.conv1(inputs)
x = self.bn1(x, training=training)
x = self.relu(x)
x = self.conv2(x)
x = self.bn2(x, training=training)
identity = self.downsample(identity)
x += identity
x = self.relu(x)
return x
def MEAN_Spot(opt):
inputs1 = keras.layers.Input(shape=(42, 42, 1))
inputs2 = keras.layers.Input(shape=(42, 42, 1))
inputs3 = keras.layers.Input(shape=(42, 42, 1))
inputs = keras.layers.Concatenate()([inputs1, inputs2, inputs3])
conv1 = keras.layers.Conv2D(3, (7, 7), padding='same', activation='relu', kernel_regularizer=l2(0.001))(inputs)
ba1 = BasicBlock(out_channels=64, kernel_size=3, strides=1)(conv1)
ba2 = BasicBlock(out_channels=64, kernel_size=3, strides=1)(ba1)
att = BasicBlock(out_channels=64, kernel_size=3, strides=1)(ba2)
merged_conv = keras.layers.Conv2D(8, (5, 5), padding='same', activation='relu', kernel_regularizer=l2(0.1))(att)
merged_pool = keras.layers.MaxPooling2D(pool_size=(2, 2), padding='same', strides=(2, 2))(merged_conv)
flat = keras.layers.Flatten()(merged_pool)
flat_do = keras.layers.Dropout(0.2)(flat)
outputs = keras.layers.Dense(1, activation='linear', name='spot')(flat_do)
model = keras.models.Model(inputs=[inputs1, inputs2, inputs3], outputs=[outputs])
model.compile(loss={'spot': 'mse'}, optimizer=opt, metrics={'spot': tf.keras.metrics.MeanAbsoluteError()})
return model
```
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