• 正文
  • 推薦器件
  • 相關推薦
申請入駐 產(chǎn)業(yè)圖譜

深度學習模型的斷點續(xù)訓、參數(shù)提取與acc、loss可視化 (全代碼)

2024/07/01
2197
加入交流群
掃碼加入
獲取工程師必備禮包
參與熱點資訊討論

斷點續(xù)訓

斷點續(xù)訓是指模型在訓練完后能保存下來,下一次訓練能保持之前的成果繼續(xù)訓練。

下面是在最簡單的識別mnist數(shù)據(jù)集的DNN基礎上逐漸加功能:

import tensorflow as tf
import os

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])

#斷點續(xù)訓!
checkpoint_save_path = "./checkpoint/mnist.ckpt"
if os.path.exists(checkpoint_save_path + '.index'):
    print('-------------load the model-----------------')
    model.load_weights(checkpoint_save_path)   #如果模型之前訓練過,就加載之前的模型繼續(xù)訓練

cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
                                                 save_weights_only=True,
                                                 save_best_only=True)   #保存參數(shù)和最好的結果

history = model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1,
                    callbacks=[cp_callback])
model.summary()

參數(shù)提取

前面保存了模型,那怎么看到模型保存的參數(shù)呢?很簡單:

import tensorflow as tf
import os
import numpy as np
np.set_printoptions(threshold=np.inf) #讓print的內(nèi)容無限制

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0
model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])
model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])
# 斷點續(xù)訓!
checkpoint_save_path = "./checkpoint/mnist.ckpt"
if os.path.exists(checkpoint_save_path + '.index'):
    print('-------------load the model-----------------')
    model.load_weights(checkpoint_save_path)
cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
                                                 save_weights_only=True,
                                                 save_best_only=True)
history = model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1,
                    callbacks=[cp_callback])
model.summary()

#參數(shù)提取!
print(model.trainable_variables)
file = open('./weights.txt', 'w') #參數(shù)保存到weights.txt里面
for v in model.trainable_variables:
    file.write(str(v.name) + 'n')
    file.write(str(v.shape) + 'n')
    file.write(str(v.numpy()) + 'n')
file.close() 

acc、loss可視化

模型訓練完后,怎么把訓練過程中訓練集和測試集的loss和accuracy畫出來?easy:

import tensorflow as tf
import os
import numpy as np
from matplotlib import pyplot as plt

np.set_printoptions(threshold=np.inf)

mnist = tf.keras.datasets.mnist
(x_train, y_train), (x_test, y_test) = mnist.load_data()
x_train, x_test = x_train / 255.0, x_test / 255.0

model = tf.keras.models.Sequential([
    tf.keras.layers.Flatten(),
    tf.keras.layers.Dense(128, activation='relu'),
    tf.keras.layers.Dense(10, activation='softmax')
])

model.compile(optimizer='adam',
              loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=False),
              metrics=['sparse_categorical_accuracy'])

# 斷點續(xù)訓!
checkpoint_save_path = "./checkpoint/mnist.ckpt"
if os.path.exists(checkpoint_save_path + '.index'):
    print('-------------load the model-----------------')
    model.load_weights(checkpoint_save_path)

cp_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_save_path,
                                                 save_weights_only=True,
                                                 save_best_only=True)

history = model.fit(x_train, y_train, batch_size=32, epochs=5, validation_data=(x_test, y_test), validation_freq=1,
                    callbacks=[cp_callback])
model.summary()


#參數(shù)提取!
print(model.trainable_variables)
file = open('./weights.txt', 'w')
for v in model.trainable_variables:
    file.write(str(v.name) + 'n')
    file.write(str(v.shape) + 'n')
    file.write(str(v.numpy()) + 'n')
file.close()


# 畫圖!顯示訓練集和驗證集的acc和loss曲線
acc = history.history['sparse_categorical_accuracy']
val_acc = history.history['val_sparse_categorical_accuracy']
loss = history.history['loss']
val_loss = history.history['val_loss']

plt.subplot(1, 2, 1)
plt.plot(acc, label='Training Accuracy')
plt.plot(val_acc, label='Validation Accuracy')
plt.title('Training and Validation Accuracy')
plt.legend()

plt.subplot(1, 2, 2)
plt.plot(loss, label='Training Loss')
plt.plot(val_loss, label='Validation Loss')
plt.title('Training and Validation Loss')
plt.legend()
plt.show()

在這里插入圖片描述

上面這個代碼就是在最基礎的keras搭建DNN的代碼上增添了斷點續(xù)訓、參數(shù)保存loss/acc可視化功能

推薦器件

更多器件
器件型號 數(shù)量 器件廠商 器件描述 數(shù)據(jù)手冊 ECAD模型 風險等級 參考價格 更多信息
KSZ8041FTLI-TR 1 Microchip Technology Inc DATACOM, ETHERNET TRANSCEIVER, PQFP48

ECAD模型

下載ECAD模型
$2.95 查看
S29WS256P0PBFW000 1 Spansion Flash, 16MX16, 80ns, PBGA84, FBGA-84
$13.17 查看
ECS-120-20-30B-TR 1 ECS International Inc Parallel - Fundamental Quartz Crystal, 12MHz Nom, ROHS COMPLIANT, MINIATURE, SMD, 4 PIN

ECAD模型

下載ECAD模型
$1 查看

相關推薦