self.img_size = tf.shape(inputs)[1:3]
时间: 2024-05-26 20:18:24 浏览: 15
这行代码是在 TensorFlow 中定义一个变量 self.img_size,它表示输入数据的图像尺寸。具体来说,tf.shape(inputs) 返回输入张量 inputs 的形状,它是一个张量。而 [1:3] 表示对该张量进行切片,取第 1 到第 2 个元素,即取输入数据的高度和宽度,这个切片操作也返回一个张量。最后,将这个张量赋值给变量 self.img_size。
相关问题
def forward(self, inputs, is_training=False, reuse=False): self.img_size = tf.shape(inputs)[1:3]什么意思
这段代码是一个神经网络模型中的前向传播函数的一部分。它的作用是计算输入数据经过网络后的输出结果。
其中,`inputs`是输入数据的张量,`tf.shape(inputs)`返回的是张量`inputs`的形状(第一个维度是样本数,后面的维度是图像的高度和宽度),`self.img_size = tf.shape(inputs)[1:3]`则是将图像高度和宽度分别赋值给类属性`self.img_size`。这样,在模型的后续操作中可以使用`self.img_size`来获取输入图像的大小信息,以便进行后续操作。参数`is_training`和`reuse`是控制网络训练和重复使用变量的标志。
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’ class Model(): def __init__(self): self.img_seq_shape=(10,128,128,3) self.img_shape=(128,128,3) self.train_img=dataset # self.test_img=dataset_T patch = int(128 / 2 ** 4) self.disc_patch = (patch, patch, 1) self.optimizer=tf.keras.optimizers.Adam(learning_rate=0.001) self.build_generator=self.build_generator() self.build_discriminator=self.build_discriminator() self.build_discriminator.compile(loss='binary_crossentropy', optimizer=self.optimizer, metrics=['accuracy']) self.build_generator.compile(loss='binary_crossentropy', optimizer=self.optimizer) img_seq_A = Input(shape=(10,128,128,3)) #输入图片 img_B = Input(shape=self.img_shape) #目标图片 fake_B = self.build_generator(img_seq_A) #生成的伪目标图片 self.build_discriminator.trainable = False valid = self.build_discriminator([img_seq_A, fake_B]) self.combined = tf.keras.models.Model([img_seq_A, img_B], [valid, fake_B]) self.combined.compile(loss=['binary_crossentropy', 'mse'], loss_weights=[1, 100], optimizer=self.optimizer,metrics=['accuracy']) 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)
以下是使用ConvRNN2D层替换ConvLSTM2D层的修改后的代码:
```python
from tensorflow.keras.layers import Conv2D, Conv2DTranspose, ConvRNN2D, Input, BatchNormalization, LeakyReLU, Flatten, Dense, Reshape
from tensorflow.keras.models import Model
class Model():
def __init__(self):
self.img_seq_shape=(10,128,128,3)
self.img_shape=(128,128,3)
self.train_img=dataset
patch = int(128 / 2 ** 4)
self.disc_patch = (patch, patch, 1)
self.optimizer=tf.keras.optimizers.Adam(learning_rate=0.001)
self.build_generator=self.build_generator()
self.build_discriminator=self.build_discriminator()
self.build_discriminator.compile(loss='binary_crossentropy',
optimizer=self.optimizer,
metrics=['accuracy'])
self.build_generator.compile(loss='binary_crossentropy',
optimizer=self.optimizer)
img_seq_A = Input(shape=(10,128,128,3)) #输入图片
img_B = Input(shape=self.img_shape) #目标图片
fake_B = self.build_generator(img_seq_A) #生成的伪目标图片
self.build_discriminator.trainable = False
valid = self.build_discriminator([img_seq_A, fake_B])
self.combined = Model([img_seq_A, img_B], [valid, fake_B])
self.combined.compile(loss=['binary_crossentropy', 'mse'],
loss_weights=[1, 100],
optimizer=self.optimizer,
metrics=['accuracy'])
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 = Conv2D(filters, kernel_size, strides, 'same')(inputs)
x = BatchNormalization()(x)
x = LeakyReLU(alpha=0.2)(x)
return x
d0 = Input(shape=(10, 128, 128, 3))
out = ConvRNN2D(filters=32, kernel_size=3, padding='same')(d0)
out = conv2d(out, 3, 1, 1)
return Model(inputs=d0, outputs=out)
def build_discriminator(self):
def d_layer(layer_input, filters, f_size=4, normalization=True):
"""Discriminator layer"""
d = Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
if normalization:
d = BatchNormalization()(d)
d = LeakyReLU(alpha=0.2)(d)
return d
img_A = Input(shape=self.img_seq_shape)
img_B = Input(shape=self.img_shape)
combined_imgs = tf.keras.layers.concatenate([img_A, img_B])
d1 = d_layer(combined_imgs, 64, normalization=False)
d2 = d_layer(d1, 128)
d3 = d_layer(d2, 256)
d4 = d_layer(d3, 512)
validity = Conv2D(1, kernel_size=4, strides=1, padding='same')(d4)
return Model([img_A, img_B], validity)
```
在代码中,我们将ConvLSTM2D层替换为了ConvRNN2D层,并添加了新的模块`convrnn`。
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