[英]Migrate Tensorflow 1.x code to Tensorflow 2.x using Keras' model class
我剛剛開始學習 Tensorflow 2.1.0 和 Keras 2.3.1 和 Python 3.7.7。
我發現這個“ 使用原型網絡的 Omniglot 字符集分類” github Jupyter Notebook,我認為它適用於 Tensorflow 1.x。
我的問題是這段代碼:
for epoch in range(num_epochs):
for episode in range(num_episodes):
# select 60 classes
episodic_classes = np.random.permutation(no_of_classes)[:num_way]
support = np.zeros([num_way, num_shot, img_height, img_width], dtype=np.float32)
query = np.zeros([num_way, num_query, img_height, img_width], dtype=np.float32)
for index, class_ in enumerate(episodic_classes):
selected = np.random.permutation(num_examples)[:num_shot + num_query]
support[index] = train_dataset[class_, selected[:num_shot]]
# 5 querypoints per classs
query[index] = train_dataset[class_, selected[num_shot:]]
support = np.expand_dims(support, axis=-1)
query = np.expand_dims(query, axis=-1)
labels = np.tile(np.arange(num_way)[:, np.newaxis], (1, num_query)).astype(np.uint8)
_, loss_, accuracy_ = sess.run([train, loss, accuracy], feed_dict={support_set: support, query_set: query, y:labels})
if (episode+1) % 10 == 0:
print('Epoch {} : Episode {} : Loss: {}, Accuracy: {}'.format(epoch+1, episode+1, loss_, accuracy_))
是否有任何教程或書籍或文章可以幫助我使用 Keras 的 model 將此代碼遷移到 Tensorflow 2.x 和 Keras?
我想從鏈接中編寫代碼,如下所示:
import numpy as np
import os
import skimage.io as io
import skimage.transform as trans
import numpy as np
from keras.models import *
from keras.layers import *
from keras.optimizers import *
from keras.callbacks import ModelCheckpoint, LearningRateScheduler
from keras import backend as keras
def unet(pretrained_weights = None,input_size = (256,256,1)):
inputs = Input(input_size)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(inputs)
conv1 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv1)
pool1 = MaxPooling2D(pool_size=(2, 2))(conv1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool1)
conv2 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv2)
pool2 = MaxPooling2D(pool_size=(2, 2))(conv2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool2)
conv3 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv3)
pool3 = MaxPooling2D(pool_size=(2, 2))(conv3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool3)
conv4 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv4)
drop4 = Dropout(0.5)(conv4)
pool4 = MaxPooling2D(pool_size=(2, 2))(drop4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(pool4)
conv5 = Conv2D(1024, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv5)
drop5 = Dropout(0.5)(conv5)
up6 = Conv2D(512, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(drop5))
merge6 = concatenate([drop4,up6], axis = 3)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge6)
conv6 = Conv2D(512, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv6)
up7 = Conv2D(256, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv6))
merge7 = concatenate([conv3,up7], axis = 3)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge7)
conv7 = Conv2D(256, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv7)
up8 = Conv2D(128, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv7))
merge8 = concatenate([conv2,up8], axis = 3)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge8)
conv8 = Conv2D(128, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv8)
up9 = Conv2D(64, 2, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(UpSampling2D(size = (2,2))(conv8))
merge9 = concatenate([conv1,up9], axis = 3)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(merge9)
conv9 = Conv2D(64, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv9 = Conv2D(2, 3, activation = 'relu', padding = 'same', kernel_initializer = 'he_normal')(conv9)
conv10 = Conv2D(1, 1, activation = 'sigmoid')(conv9)
model = Model(input = inputs, output = conv10)
model.compile(optimizer = Adam(lr = 1e-4), loss = 'binary_crossentropy', metrics = ['accuracy'])
#model.summary()
if(pretrained_weights):
model.load_weights(pretrained_weights)
return model
在train.py
中:
model = unet(...)
model.compile(...)
model.fit(...)
Tensorflow 的這篇教程總結了一切。
最重要的是Sessions
不存在了,model 應該使用tensorflow.keras.layers
創建。
現在,在訓練 model 時,您有 2 個選擇,您可以使用 Keras 方式,也可以使用GradientTape
(這有點舊方式)。
這意味着您有兩種選擇,一種不會對您的代碼產生太大影響(GradientTape),另一種只會讓您改變一些事情(Keras)。
GradientTape 用於您想要自己循環並計算所需的漸變,它有點像 Tensorflow 1.X。
import tensorflow as tf
def unet(...):
inputs = tf.keras.layers.Input(shape_images)
...
model = Model(input = inputs, output = conv10)
model.compile(...)
return model
...
model = unet(...)
mse = tf.keras.losses.MeanSquaredError()
optimizer = tf.keras.optimizer.Adam(lr=1e-4)
然后,您像往常一樣進行訓練,只是將舊的 Session 機制替換為 GradientTape:
for epoch in range(num_epochs):
for episode in range(num_episodes):
# select 60 classes
episodic_classes = np.random.permutation(no_of_classes)[:num_way]
support = np.zeros([num_way, num_shot, img_height, img_width], dtype=np.float32)
query = np.zeros([num_way, num_query, img_height, img_width], dtype=np.float32)
for index, class_ in enumerate(episodic_classes):
selected = np.random.permutation(num_examples)[:num_shot + num_query]
support[index] = train_dataset[class_, selected[:num_shot]]
# 5 querypoints per classs
query[index] = train_dataset[class_, selected[num_shot:]]
support = np.expand_dims(support, axis=-1)
query = np.expand_dims(query, axis=-1)
labels = np.tile(np.arange(num_way)[:, np.newaxis], (1, num_query)).astype(np.uint8)
# No session here but a Gradient computing
with tf.GradientTape() as tape:
prediction = model(support) # or whatever you need as input of model
loss = mse(label, prediction)
# apply gradient descent
grads = tape.gradient(loss, model.trainable_weights)
optimizer.apply_gradients(zip(grads, model.trainable_weights))
對於 keras,您需要更改提供數據的方式,因為使用fit
,您將沒有 for 循環,而您需要實現Generator或任何可以迭代的數據結構。 這意味着您基本上需要(X, y)
的列表。 data_struct[0] 將為您提供第一個 X,Y 對。
一旦你有了這個數據結構,就很容易了。
像 GradientTape 一樣定義 model
像 GradientTape 一樣定義優化器
編譯 model
model.compile(loss='binary_crossentropy', optimizer=optimizer, metrics=['accuracy']) # Or whatever you need as loss/metrics
model.fit(data_struct, epochs=500) # Add validation_data if you want, callback ...
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