def build_model(self, input_shape, nb_classes): x = keras.layers.Input(input_shape) conv1 = keras.layers.Conv1D(128, 8, 1, padding='same')(x) conv1 = keras.layers.BatchNormalization()(conv1) conv1 = keras.layers.Activation('relu')(conv1) conv2 = keras.layers.Conv1D(256, 5, 1, padding='same')(conv1) conv2 = keras.layers.BatchNormalization()(conv2) conv2 = keras.layers.Activation('relu')(conv2) conv3 = keras.layers.Conv1D(128, 3, 1, padding='same')(conv2) conv3 = keras.layers.BatchNormalization()(conv3) conv3 = keras.layers.Activation('relu')(conv3) full = keras.layers.GlobalAveragePooling1D()(conv3) out = keras.layers.Dense(nb_classes, activation='softmax')(full) model = keras.models.Model(inputs=x, outputs=out) # optimizer = keras.optimizers.Adam() optimizer = keras.optimizers.Nadam() model.compile(loss='categorical_crossentropy', optimizer=optimizer, metrics=['accuracy']) return model
时间: 2023-06-06 22:05:44 浏览: 63
这是在 Keras 框架中定义一个模型的代码。这个模型由输入层、三个卷积层、全局平均池化层、全连接层和输出层组成。卷积层之间还有批归一化层和激活层。这个模型的输入尺寸为 `input_shape` ,输出的类别数为 `nb_classes` 。
相关问题
修改下面代码使其具体生成器的卷积lstm更换为卷积RNN def build_generator(self): def res_net(inputs, filters): x = inputs net = conv2d(x, filters // 2, (1, 1), 1) net = conv2d(net, filters, (3, 3), 1) net = net + x # net=tf.keras.layers.LeakyReLU(0.2)(net) return net def conv2d(inputs, filters, kernel_size, strides): x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs) x = tf.keras.layers.BatchNormalization()(x) x = tf.keras.layers.LeakyReLU(alpha=0.2)(x) return x d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3)) out= tf.keras.layers.ConvLSTM2D(filters=32, kernel_size=3,padding='same')(d0) out=tf.keras.layers.Conv2D(3,1,1,'same')(out) return keras.Model(inputs=d0, outputs=out)
修改如下:
```python
def build_generator(self):
def res_net(inputs, filters):
x = inputs
net = conv2d(x, filters // 2, (1, 1), 1)
net = conv2d(net, filters, (3, 3), 1)
net = net + x
return net
def conv2d(inputs, filters, kernel_size, strides):
x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
return x
d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3))
out = tf.keras.layers.ConvRNN2D(filters=32, kernel_size=3, padding='same')(d0)
out = tf.keras.layers.Conv2D(3, 1, 1, 'same')(out)
return keras.Model(inputs=d0, outputs=out)
```
将 `ConvLSTM2D` 替换为 `ConvRNN2D` 即可,它们的参数意义和使用方法都是一样的。
将下面代码使用ConvRNN2D层来替换ConvLSTM2D层,并在模块__init__.py中创建类‘convrnn’ def build_generator(self): def res_net(inputs, filters): x = inputs net = conv2d(x, filters // 2, (1, 1), 1) net = conv2d(net, filters, (3, 3), 1) net = net + x # net=tf.keras.layers.LeakyReLU(0.2)(net) return net def conv2d(inputs, filters, kernel_size, strides): x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs) x = tf.keras.layers.BatchNormalization()(x) x = tf.keras.layers.LeakyReLU(alpha=0.2)(x) return x d0 = tf.keras.layers.Input(shape=(10, 128, 128, 3)) out= tf.keras.layers.ConvRNN2D(filters=32, kernel_size=3,padding='same')(d0) out=tf.keras.layers.Conv2D(3,1,1,'same')(out) return keras.Model(inputs=d0, outputs=out)
请注意,ConvRNN2D层在Keras中是一个实验性的层,需要安装Tensorflow 2.0才能使用。下面是使用ConvRNN2D替换ConvLSTM2D的代码:
```
from tensorflow.keras.layers import ConvRNN2D
class Generator():
def __init__(self, input_shape=(10, 128, 128, 3)):
self.input_shape = input_shape
def build_generator(self):
def res_net(inputs, filters):
x = inputs
net = conv2d(x, filters // 2, (1, 1), 1)
net = conv2d(net, filters, (3, 3), 1)
net = net + x
return net
def conv2d(inputs, filters, kernel_size, strides):
x = tf.keras.layers.Conv2D(filters, kernel_size, strides, 'same')(inputs)
x = tf.keras.layers.BatchNormalization()(x)
x = tf.keras.layers.LeakyReLU(alpha=0.2)(x)
return x
d0 = tf.keras.layers.Input(shape=self.input_shape)
out = ConvRNN2D(filters=32, kernel_size=(3,3), padding='same', return_sequences=False)(d0)
out = tf.keras.layers.Conv2D(3, 1, 1, 'same')(out)
return tf.keras.Model(inputs=d0, outputs=out)
model = Generator().build_generator()
model.summary()
```
请注意,ConvRNN2D层的输入形状应该是(batch_size, time_steps, rows, cols, filters),因此我们将输入形状设置为(10, 128, 128, 3),其中10是时间步长。ConvRNN2D层的输出形状是(batch_size, rows, cols, filters),因此我们需要使用return_sequences=False来指示输出一个张量,而不是一个序列。
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