创建一个模型并编译它以从图像中提取嵌入

Question

我正在尝试将这段代码3.3 Omniglot Character set classification using Prototypical Network.ipynb从Tensorflow 1.1迁移到Tensorflow 2.x

我的怀疑是我不知道我到底在做什么。我遇到问题的代码是：

import numpy as np
import tensorflow as tf

def convolution_block(inputs, out_channels, name='conv'):

    conv = tf.layers.conv2d(inputs, out_channels, kernel_size=3, padding='SAME')
    conv = tf.contrib.layers.batch_norm(conv, updates_collections=None, decay=0.99, scale=True, center=True)
    conv = tf.nn.relu(conv)
    conv = tf.contrib.layers.max_pool2d(conv, 2)
    
    return conv

def get_embeddings(support_set, h_dim, z_dim, reuse=False):

    net = convolution_block(support_set, h_dim)
    net = convolution_block(net, h_dim)
    net = convolution_block(net, h_dim) 
    net = convolution_block(net, z_dim) 
    net = tf.contrib.layers.flatten(net)
        
    return net

我已经迁移到：

import tensorflow as tf
from tensorflow.keras.layers import Input, Conv2D, MaxPooling2D

def get_embedding_function(img_shape):
    inputs = Input(img_shape)
    conv1 = Conv2D(64, (3, 3), activation='relu', padding='same', name='conv1_1')(inputs)
    pool1 = MaxPooling2D(pool_size=(2, 2), data_format='channels_last', name='pool1')(conv1)

    conv2 = Conv2D(96, (3, 3), activation='relu', padding='same', name='conv2_1')(pool1)
    pool2 = MaxPooling2D(pool_size=(2, 2), data_format='channels_last', name='pool2')(conv2)

    conv3 = Conv2D(128, (3, 3), activation='relu', padding='same', name='conv3_1')(pool2)
    pool3 = MaxPooling2D(pool_size=(2, 2), data_format='channels_last', name='pool3')(conv3)

    conv4 = Conv2D(256, (3, 3), activation='relu', padding='same', name='conv4_1')(pool3)
    pool4 = MaxPooling2D(pool_size=(2, 2), data_format='channels_last', name='pool4')(conv4)

    model = tf.keras.models.Model(inputs=inputs, outputs=pool4)

    model.compile(tf.keras.optimizers.Adam(lr=(1e-4) * 2), loss='binary_crossentropy', metrics=['accuracy'])

    return model

此函数的层与前一个函数的层不同，因为我想测试自己的网络

我将使用此函数从图像中提取特征

我不得不添加model = tf.keras.models.Model(inputs=inputs, outputs=pool4)，因为如果我只返回pool4，它就不起作用了。我还添加了model.compile(tf.keras.optimizers.Adam(lr=(1e-4) * 2), loss='binary_crossentropy', metrics=['accuracy'])，但是我不知道我是否需要它。

我是否需要创建模型并编译它以从图像中提取特征

Answer 1

编译模型与否取决于您用于训练模型的方法，即：

如果使用model.fit，则需要在拟合模型之前编译模型

如果使用custom training，则不必返回模型并使用它，就像：

optimizer = tf.keras.optimizers.Adam(lr=(1e-4) * 2)
bce = tf.keras.losses.BinaryCrossentropy()

tf.GradientTape() as tape:
    y_pred = model(X)
    loss = bce(y_true, y_pred)

grads = tape.gradient(loss , model.trainable_variables)
optimizer.apply_gradients(zip(grads , model.trainable_variables))