layer = tf.layers.conv3d
时间: 2023-05-02 13:00:42 浏览: 58
b'layer = tf.layers.conv3d' 的意思是创建一个用于卷积的 3D 层。其中,tf 是 TensorFlow 库的缩写,layers 是 TensorFlow 中用于定义神经网络层的模块名称,conv3d 表示该层为 3D 卷积层。
相关问题
conv_layer = layers.Conv1D
layers.Conv1D是TensorFlow中的一个函数,用于创建一维卷积层。这个函数可以接受一个输入张量和一些参数,然后返回一个经过卷积操作后的输出张量。在使用layers.Conv1D时,你需要注意以下几点:
- 输入张量的维度应该是(batch_size, seq_length, embedding_dim),其中batch_size表示每次输入的文本数量,seq_length表示每个文本的词语数或者单字数,embedding_dim表示每个词语或者每个字的向量长度。
- filters参数指定了卷积核(过滤器)的数目。
- kernel_size参数指定了卷积核的大小,卷积核可以看做是一个滑窗,它沿着输入张量的seq_length维度进行滑动。
- 执行卷积操作后,会得到一个输出张量,其维度为(batch_size, seq_length - kernel_size + 1, filters)。
下面是一个使用layers.Conv1D的代码示例:
```python
import tensorflow as tf
num_filters = 2
kernel_size = 2
batch_size = 1
seq_length = 4
embedding_dim = 5
embedding_inputs = tf.constant(-1.0, shape=[batch_size, seq_length, embedding_dim], dtype=tf.float32)
conv = tf.layers.Conv1D(num_filters, kernel_size)(embedding_inputs)
session = tf.Session()
session.run(tf.global_variables_initializer())
print(session.run(conv).shape)
```
以上代码创建了一个卷积层,并对输入张量进行卷积操作。最后打印输出张量的形状。
下面代码在tensorflow中出现了init() missing 1 required positional argument: 'cell'报错: 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= 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) def build_discriminator(self): def d_layer(layer_input, filters, f_size=4, bn=True): d = tf.keras.layers.Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input) if bn: d = tf.keras.layers.BatchNormalization(momentum=0.8)(d) d = tf.keras.layers.LeakyReLU(alpha=0.2)(d) return d img_A = tf.keras.layers.Input(shape=(10, 128, 128, 3)) img_B = tf.keras.layers.Input(shape=(128, 128, 3)) df = 32 lstm_out = ConvRNN2D(filters=df, kernel_size=4, padding="same")(img_A) lstm_out = tf.keras.layers.LeakyReLU(alpha=0.2)(lstm_out) combined_imgs = tf.keras.layers.Concatenate(axis=-1)([lstm_out, img_B]) d1 = d_layer(combined_imgs, df)#64 d2 = d_layer(d1, df * 2)#32 d3 = d_layer(d2, df * 4)#16 d4 = d_layer(d3, df * 8)#8 validity = tf.keras.layers.Conv2D(1, kernel_size=4, strides=1, padding='same')(d4) return tf.keras.Model([img_A, img_B], validity)
你在定义 Model 类时,没有传入参数 cell,但是在代码中使用了 ConvRNN2D 的实例化对象,这个对象需要一个 cell 参数。你需要在初始化函数中添加这个参数,如下所示:
```
class Model():
def __init__(self, cell):
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(cell)
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))
img_B = Input(shape=self.img_shape)
fake_B = self.build_generator(img_seq_A)
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
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= ConvRNN2D(cell, 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)
def build_discriminator(self, cell):
def d_layer(layer_input, filters, f_size=4, bn=True):
d = tf.keras.layers.Conv2D(filters, kernel_size=f_size, strides=2, padding='same')(layer_input)
if bn:
d = tf.keras.layers.BatchNormalization(momentum=0.8)(d)
d = tf.keras.layers.LeakyReLU(alpha=0.2)(d)
return d
img_A = tf.keras.layers.Input(shape=(10, 128, 128, 3))
img_B = tf.keras.layers.Input(shape=(128, 128, 3))
df = 32
lstm_out = ConvRNN2D(cell, filters=df, kernel_size=4, padding="same")(img_A)
lstm_out = tf.keras.layers.LeakyReLU(alpha=0.2)(lstm_out)
combined_imgs = tf.keras.layers.Concatenate(axis=-1)([lstm_out, img_B])
d1 = d_layer(combined_imgs, df)#64
d2 = d_layer(d1, df * 2)#32
d3 = d_layer(d2, df * 4)#16
d4 = d_layer(d3, df * 8)#8
validity = tf.keras.layers.Conv2D(1, kernel_size=4, strides=1, padding='same')(d4)
return tf.keras.Model([img_A, img_B], validity)
```