Day 59：使用 TensorFlow、PyTorch 深度学习进行项目实战
发布日期：2022年3月14日 14:57 阅读: 239 访问: 240
深度学习工程师有时候也被称作"炼丹师"。为什么呢？因为他们的工作和"炼丹"非常相似。 古时候的炼丹师的日常主要工作是准备药材，架起炼丹炉，点火炼丹，开炉验丹。 而深度学习工程师的日常主要工作是准备数据，架起模型，训练模型，验证模型。 药材品质对于仙丹成败，就好像数据质量对于模型效果，是第一重要的。 炼丹炉的品质就
深度学习工程师有时候也被称作"炼丹师"。为什么呢？因为他们的工作和"炼丹"非常相似。
- 古时候的炼丹师的日常主要工作是准备药材，架起炼丹炉，点火炼丹，开炉验丹。
- 而深度学习工程师的日常主要工作是准备数据，架起模型，训练模型，验证模型。
- 药材品质对于仙丹成败，就好像数据质量对于模型效果，是第一重要的。
- 炼丹炉的品质就好像模型的结构和目标函数的选择，是第二重要的。
- 炼丹过程火候的掌控就好像训练模型的优化算法的选择，是第三重要的。
目前深度学习界的炼丹师使用的炼丹炉主要生产自两个品牌厂商，一个叫做 TensorFlow，另一个叫做 PyTorch。
TensorFlow 这个品牌的炼丹炉历史悠久，2015 年就横空出世，使用它的炼丹师最多。这个品牌的炼丹炉的优点是功能强大，类似炼丹全家桶这种东西，缺点是稍有些复杂，不少炼丹师抱怨这个炼丹炉出问题时调试起来很崩溃，目前出了 TensorFlow 2 版本后这个问题好了许多。
PyTorch 这个炼丹炉品牌历史较短，2017 年才营业开张。不同于 TensorFlow 这个品牌追求炼丹全家桶，PyTorch 的特点是小而美，没有放进太多非核心的功能，有需要的时候炼丹师可以利用其它工具 DIY。由于其小而美的小透明风格，学术圈的大部分炼丹师都热衷于使用它来花式炼丹，并发表了大量的 Paper。
下面我们将通过一个二分类问题的小范例，演示使用 TensorFlow 2 和 PyTorch 进行炼丹的一般流程，让同学们感受一下炼丹的乐趣。
对于初次接触深度学习项目的同学，项目中的许多细节可能不太能看懂。
不必慌张，把握炼丹的整体流程是最关键的，相关的技术细节可以在需要的时候花时间各个击破。
TensorFlow 2 还是 PyTorch？
先说结论：
- 如果是工程师，应该优先选 TensorFlow 2；
- 如果是学生或者研究人员，应该优先选择 PyTorch；
- 如果时间足够，最好 TensorFlow 2 和 PyTorch 都要学习掌握。
理由如下：
- 在工业界最重要的是模型落地，目前国内的大部分互联网企业只支持 TensorFlow 模型的在线部署，不支持 PyTorch。并且工业界更加注重的是模型的高可用性，许多时候使用的都是成熟的模型架构，调试需求并不大。
- 研究人员最重要的是快速迭代发表文章，需要尝试一些较新的模型架构。而 PyTorch 在易用性上相比 TensorFlow 2 有一些优势，更加方便调试。并且在 2019 年以来在学术界占领了大半壁江山，能够找到的相应最新研究成果更多。
- TensorFlow 2 和 PyTorch 实际上整体风格已经非常相似，学会其中一个，学习另外一个将比较容易。两种框架都掌握的话，能够参考的开源模型案例更多，并且可以方便地在两种框架之间切换。
深度学习建模的一般流程
无论使用任何框架，深度学习构建神经网络模型的一般流程包括：
- 准备数据
- 定义模型
- 训练模型
- 评估模型
- 使用模型
- 保存模型
下面我们将通过一个简单的二分类问题为例，对比演示使用 TensorFlow 2 和 PyTorch 建立模型的一般流程。
TensorFlow 建模流程范例
TensorFlow 一般使用 DataSet 构建数据管道，然后通过 tf.keras 构建模型，编译模型后调用 fit 方法将数据喂入模型开始训练。
准备数据
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import tensorflow as tf
from tensorflow.keras import layers,losses,metrics,optimizers
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
#正负样本数量
n_positive,n_negative = 2000,2000
#生成正样本，小圆环分布
r_p = 5.0 + tf.random.truncated_normal([n_positive,1],0.0,1.0)
theta_p = tf.random.uniform([n_positive,1],0.0,2*np.pi)
Xp = tf.concat([r_p*tf.cos(theta_p),r_p*tf.sin(theta_p)],axis = 1)
Yp = tf.ones_like(r_p)
#生成负样本，大圆环分布
r_n = 8.0 + tf.random.truncated_normal([n_negative,1],0.0,1.0)
theta_n = tf.random.uniform([n_negative,1],0.0,2*np.pi)
Xn = tf.concat([r_n*tf.cos(theta_n),r_n*tf.sin(theta_n)],axis = 1)
Yn = tf.zeros_like(r_n)
#汇总样本
X = tf.concat([Xp,Xn],axis = 0)
Y = tf.concat([Yp,Yn],axis = 0)
#样本洗牌
data = tf.concat([X,Y],axis = 1)
data = tf.random.shuffle(data)
X = data[:,:2]
Y = data[:,2:]
#可视化
plt.figure(figsize = (6,6))
plt.scatter(Xp[:,0].numpy(),Xp[:,1].numpy(),c = "r")
plt.scatter(Xn[:,0].numpy(),Xn[:,1].numpy(),c = "g")
plt.legend(["positive","negative"]);
# TensorFlow 一般使用 Dataset 来构建数据管道
n = n_positive + n_negative
ds_train = tf.data.Dataset.from_tensor_slices((X[0:n*3//4,:],Y[0:n*3//4,:])) \
.shuffle(buffer_size = 1000).batch(20) \
.prefetch(tf.data.experimental.AUTOTUNE) \
.cache()
ds_valid = tf.data.Dataset.from_tensor_slices((X[n*3//4:,:],Y[n*3//4:,:])) \
.batch(20) \
.prefetch(tf.data.experimental.AUTOTUNE) \
.cache()
定义模型
TensorFlow 一般有以下 3 种方式构建模型：使用 Sequential 按层顺序构建模型，使用函数式 API 构建任意结构模型，继承 Model 基类构建自定义模型。
此处选择使用函数式 API 构建模型。
tf.keras.backend.clear_session()
x_input = layers.Input(shape = (2,))
x = layers.Dense(4,activation = "relu",name = "dense1")(x_input)
x = layers.Dense(8,activation = "relu",name = "dense2")(x)
y = layers.Dense(1,activation = "sigmoid",name = "dense3")(x)
model = tf.keras.Model(inputs = [x_input],outputs = [y] )
model.summary()
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 2)] 0
_________________________________________________________________
dense1 (Dense) (None, 4) 12
_________________________________________________________________
dense2 (Dense) (None, 8) 40
_________________________________________________________________
dense3 (Dense) (None, 1) 9
=================================================================
Total params: 61
Trainable params: 61
Non-trainable params: 0
_________________________________________________________________
训练模型
model.compile(optimizer="adam",loss="binary_crossentropy",metrics=["accuracy"])
history = model.fit(ds_train,epochs= 50,validation_data= ds_valid)
Epoch 45/50
150/150 [==============================] - 0s 2ms/step - loss: 0.1424 - accuracy: 0.9360 - val_loss: 0.1317 - val_accuracy: 0.9490
Epoch 46/50
150/150 [==============================] - 0s 2ms/step - loss: 0.1412 - accuracy: 0.9360 - val_loss: 0.1306 - val_accuracy: 0.9490
Epoch 47/50
150/150 [==============================] - 0s 2ms/step - loss: 0.1401 - accuracy: 0.9370 - val_loss: 0.1298 - val_accuracy: 0.9480
Epoch 48/50
150/150 [==============================] - 0s 3ms/step - loss: 0.1392 - accuracy: 0.9373 - val_loss: 0.1288 - val_accuracy: 0.9480
Epoch 49/50
150/150 [==============================] - 0s 3ms/step - loss: 0.1383 - accuracy: 0.9373 - val_loss: 0.1279 - val_accuracy: 0.9480
Epoch 50/50
150/150 [==============================] - 0s 3ms/step - loss: 0.1375 - accuracy: 0.9380 - val_loss: 0.1271 - val_accuracy: 0.9480
评估模型
# 结果可视化
fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize = (12,5))
ax1.scatter(Xp[:,0].numpy(),Xp[:,1].numpy(),c = "r")
ax1.scatter(Xn[:,0].numpy(),Xn[:,1].numpy(),c = "g")
ax1.legend(["positive","negative"]);
ax1.set_title("y_true");
Xp_pred = tf.boolean_mask(X,tf.squeeze(model(X)>=0.5),axis = 0)
Xn_pred = tf.boolean_mask(X,tf.squeeze(model(X)<0.5),axis = 0)
ax2.scatter(Xp_pred[:,0].numpy(),Xp_pred[:,1].numpy(),c = "r")
ax2.scatter(Xn_pred[:,0].numpy(),Xn_pred[:,1].numpy(),c = "g")
ax2.legend(["positive","negative"]);
ax2.set_title("y_pred");
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import matplotlib.pyplot as plt
def plot_metric(history, metric):
train_metrics = history.history[metric]
val_metrics = history.history['val_'+metric]
epochs = range(1, len(train_metrics) + 1)
plt.plot(epochs, train_metrics, 'bo--')
plt.plot(epochs, val_metrics, 'ro-')
plt.title('Training and validation '+ metric)
plt.xlabel("Epochs")
plt.ylabel(metric)
plt.legend(["train_"+metric, 'val_'+metric])
plt.show()
plot_metric(history,"loss")
plot_metric(history,"accuracy")
可以用 model.evaluate 评估模型。
(loss,accuracy) = model.evaluate(ds_valid)
print(loss,accuracy)
50/50 [==============================] - 0s 1ms/step - loss: 0.1114 - accuracy: 0.9510
0.11143173044547439 0.951
使用模型
一般使用 model.predict 方法进行预测。
model.predict(ds_valid)[0:10]
array([[9.8861283e-01],
[2.2271587e-02],
[2.0001957e-04],
[2.8627261e-03],
[7.2502601e-01],
[9.9810719e-01],
[9.7249800e-01],
[1.6852912e-01],
[3.1919468e-02],
[2.7522160e-02]], dtype=float32)
保存模型
可以用 save 方法保存模型：
# 保存模型结构与模型参数到文件，该方式保存的模型具有跨平台性便于部署
model.save('./data/tf_model_savedmodel', save_format="tf")
print('export saved model.')
model_loaded = tf.keras.models.load_model('./data/tf_model_savedmodel')
loss,accuracy = model_loaded.evaluate(ds_valid)
print(loss,accuracy)
INFO:tensorflow:Assets written to: ./data/tf_model_savedmodel/assets
export saved model.
50/50 [==============================] - 0s 4ms/step - loss: 0.1114 - accuracy: 0.9510
0.11143159026280046 0.951
PyTorch 建模流程范例
PyTorch 一般使用 DataLoader 加载数据管道，然后继承 nn.Module 构建模型，然后编写自定义训练循环。
import os
#Mac 系统上 PyTorch 和 Matplotlib 在 Jupyter 中同时跑需要更改环境变量
os.environ["KMP_DUPLICATE_LIB_OK"]="TRUE"
准备数据
import numpy as np
import pandas as pd
from matplotlib import pyplot as plt
import torch
from torch import nn
import torch.nn.functional as F
from torch.utils.data import Dataset,DataLoader,TensorDataset
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
#正负样本数量
n_positive,n_negative = 2000,2000
#生成正样本，小圆环分布
r_p = 5.0 + torch.normal(0.0,1.0,size = [n_positive,1])
theta_p = 2*np.pi*torch.rand([n_positive,1])
Xp = torch.cat([r_p*torch.cos(theta_p),r_p*torch.sin(theta_p)],axis = 1)
Yp = torch.ones_like(r_p)
#生成负样本，大圆环分布
r_n = 8.0 + torch.normal(0.0,1.0,size = [n_negative,1])
theta_n = 2*np.pi*torch.rand([n_negative,1])
Xn = torch.cat([r_n*torch.cos(theta_n),r_n*torch.sin(theta_n)],axis = 1)
Yn = torch.zeros_like(r_n)
#汇总样本
X = torch.cat([Xp,Xn],axis = 0)
Y = torch.cat([Yp,Yn],axis = 0)
#可视化
plt.figure(figsize = (6,6))
plt.scatter(Xp[:,0],Xp[:,1],c = "r")
plt.scatter(Xn[:,0],Xn[:,1],c = "g")
plt.legend(["positive","negative"]);
#构建输入数据管道
from sklearn.model_selection import train_test_split
X_train,X_valid,Y_train,Y_valid = train_test_split(X.numpy(),Y.numpy(),test_size = 0.3)
ds_train= TensorDataset(torch.from_numpy(X_train),torch.from_numpy(Y_train))
ds_valid = TensorDataset(torch.from_numpy(X_valid),torch.from_numpy(Y_valid))
dl_train = DataLoader(ds_train,batch_size = 10,shuffle=True,num_workers=2)
dl_valid = DataLoader(ds_valid,batch_size = 10,num_workers=2)
定义模型
from torchsummary import summary
class Net(nn.Module):
def __init__(self):
super(Net, self).__init__()
self.fc1 = nn.Linear(2,4)
self.fc2 = nn.Linear(4,8)
self.fc3 = nn.Linear(8,1)
# 正向传播
def forward(self,x):
x = F.relu(self.fc1(x))
x = F.relu(self.fc2(x))
y = nn.Sigmoid()(self.fc3(x))
return y
net = Net()
summary(net,input_size= (2,))
----------------------------------------------------------------
Layer (type) Output Shape Param #
================================================================
Linear-1 [-1, 4] 12
Linear-2 [-1, 8] 40
Linear-3 [-1, 1] 9
================================================================
Total params: 61
Trainable params: 61
Non-trainable params: 0
----------------------------------------------------------------
Input size (MB): 0.00
Forward/backward pass size (MB): 0.00
Params size (MB): 0.00
Estimated Total Size (MB): 0.00
----------------------------------------------------------------
训练模型
PyTorch 通常需要用户编写自定义训练循环，训练循环的代码风格因人而异。
有 3 类典型的训练循环代码风格：脚本形式训练循环、函数形式训练循环、类形式训练循环。
此处介绍一种较通用的脚本形式。
from sklearn.metrics import accuracy_score
import datetime
loss_func = nn.BCELoss()
optimizer = torch.optim.Adam(params=net.parameters(),lr = 0.001)
metric_func = lambda y_pred,y_true: accuracy_score(y_true.data.numpy(),y_pred.data.numpy()>0.5)
metric_name = "accuracy"
epochs = 20
log_step_freq = 100
dfhistory = pd.DataFrame(columns = ["epoch","loss",metric_name,"val_loss","val_"+metric_name])
print("Start Training...")
nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print("=========="*8 + "%s"%nowtime)
for epoch in range(1,epochs+1):
# 1，训练循环-------------------------------------------------
net.train()
loss_sum = 0.0
metric_sum = 0.0
step = 1
for step, (features,labels) in enumerate(dl_train, 1):
# 梯度清零
optimizer.zero_grad()
# 正向传播求损失
predictions = net(features)
loss = loss_func(predictions,labels)
metric = metric_func(predictions,labels)
# 反向传播求梯度
loss.backward()
optimizer.step()
# 打印 batch 级别日志
loss_sum += loss.item()
metric_sum += metric.item()
if step%log_step_freq == 0:
print(("[step = %d] loss: %.3f, "+metric_name+": %.3f") %
(step, loss_sum/step, metric_sum/step))
# 2，验证循环-------------------------------------------------
net.eval()
val_loss_sum = 0.0
val_metric_sum = 0.0
val_step = 1
for val_step, (features,labels) in enumerate(dl_valid, 1):
predictions = net(features)
val_loss = loss_func(predictions,labels)
val_metric = metric_func(predictions,labels)
val_loss_sum += val_loss.item()
val_metric_sum += val_metric.item()
# 3，记录日志-------------------------------------------------
info = (epoch, loss_sum/step, metric_sum/step,
val_loss_sum/val_step, val_metric_sum/val_step)
dfhistory.loc[epoch-1] = info
# 打印 epoch 级别日志
print(("\nEPOCH = %d, loss = %.3f,"+ metric_name + \
" = %.3f, val_loss = %.3f, "+"val_"+ metric_name+" = %.3f")
%info)
nowtime = datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S')
print("\n"+"=========="*8 + "%s"%nowtime)
print('Finished Training...')
Start Training...
================================================================================2020-05-03 00:07:07
[step = 100] loss: 0.783, accuracy: 0.511
[step = 200] loss: 0.740, accuracy: 0.507
EPOCH = 1, loss = 0.723,accuracy = 0.513, val_loss = 0.684, val_accuracy = 0.514
================================================================================2020-05-03 00:07:08
[step = 100] loss: 0.679, accuracy: 0.518
[step = 200] loss: 0.675, accuracy: 0.521
EPOCH = 2, loss = 0.672,accuracy = 0.530, val_loss = 0.674, val_accuracy = 0.528
================================================================================2020-05-03 00:07:09
[step = 100] loss: 0.660, accuracy: 0.553
[step = 200] loss: 0.661, accuracy: 0.549
EPOCH = 3, loss = 0.660,accuracy = 0.551, val_loss = 0.663, val_accuracy = 0.546
================================================================================2020-05-03 00:07:10
[step = 100] loss: 0.642, accuracy: 0.578
[step = 200] loss: 0.648, accuracy: 0.580
EPOCH = 4, loss = 0.647,accuracy = 0.580, val_loss = 0.651, val_accuracy = 0.578
================================================================================2020-05-03 00:07:11
[step = 100] loss: 0.634, accuracy: 0.600
[step = 200] loss: 0.630, accuracy: 0.611
EPOCH = 5, loss = 0.630,accuracy = 0.612, val_loss = 0.632, val_accuracy = 0.640
================================================================================2020-05-03 00:07:12
[step = 100] loss: 0.619, accuracy: 0.692
[step = 200] loss: 0.615, accuracy: 0.660
EPOCH = 6, loss = 0.608,accuracy = 0.670, val_loss = 0.607, val_accuracy = 0.674
================================================================================2020-05-03 00:07:13
[step = 100] loss: 0.595, accuracy: 0.704
[step = 200] loss: 0.581, accuracy: 0.715
EPOCH = 7, loss = 0.577,accuracy = 0.717, val_loss = 0.573, val_accuracy = 0.716
================================================================================2020-05-03 00:07:14
[step = 100] loss: 0.546, accuracy: 0.748
[step = 200] loss: 0.539, accuracy: 0.744
EPOCH = 8, loss = 0.533,accuracy = 0.753, val_loss = 0.513, val_accuracy = 0.783
================================================================================2020-05-03 00:07:15
[step = 100] loss: 0.486, accuracy: 0.794
[step = 200] loss: 0.477, accuracy: 0.799
EPOCH = 9, loss = 0.470,accuracy = 0.799, val_loss = 0.462, val_accuracy = 0.786
================================================================================2020-05-03 00:07:16
[step = 100] loss: 0.410, accuracy: 0.834
[step = 200] loss: 0.427, accuracy: 0.811
EPOCH = 10, loss = 0.420,accuracy = 0.816, val_loss = 0.417, val_accuracy = 0.803
================================================================================2020-05-03 00:07:17
[step = 100] loss: 0.392, accuracy: 0.828
[step = 200] loss: 0.378, accuracy: 0.829
EPOCH = 11, loss = 0.376,accuracy = 0.831, val_loss = 0.374, val_accuracy = 0.833
================================================================================2020-05-03 00:07:18
[step = 100] loss: 0.339, accuracy: 0.849
[step = 200] loss: 0.346, accuracy: 0.843
EPOCH = 12, loss = 0.340,accuracy = 0.846, val_loss = 0.345, val_accuracy = 0.827
================================================================================2020-05-03 00:07:19
[step = 100] loss: 0.307, accuracy: 0.865
[step = 200] loss: 0.315, accuracy: 0.849
EPOCH = 13, loss = 0.312,accuracy = 0.850, val_loss = 0.313, val_accuracy = 0.848
================================================================================2020-05-03 00:07:20
[step = 100] loss: 0.298, accuracy: 0.856
[step = 200] loss: 0.290, accuracy: 0.862
EPOCH = 14, loss = 0.288,accuracy = 0.861, val_loss = 0.299, val_accuracy = 0.845
================================================================================2020-05-03 00:07:21
[step = 100] loss: 0.272, accuracy: 0.869
[step = 200] loss: 0.271, accuracy: 0.869
EPOCH = 15, loss = 0.271,accuracy = 0.866, val_loss = 0.282, val_accuracy = 0.855
================================================================================2020-05-03 00:07:22
[step = 100] loss: 0.274, accuracy: 0.872
[step = 200] loss: 0.262, accuracy: 0.876
EPOCH = 16, loss = 0.258,accuracy = 0.879, val_loss = 0.267, val_accuracy = 0.868
================================================================================2020-05-03 00:07:22
[step = 100] loss: 0.241, accuracy: 0.904
[step = 200] loss: 0.244, accuracy: 0.907
EPOCH = 17, loss = 0.246,accuracy = 0.910, val_loss = 0.264, val_accuracy = 0.910
================================================================================2020-05-03 00:07:23
[step = 100] loss: 0.237, accuracy: 0.916
[step = 200] loss: 0.239, accuracy: 0.917
EPOCH = 18, loss = 0.239,accuracy = 0.918, val_loss = 0.255, val_accuracy = 0.913
================================================================================2020-05-03 00:07:24
[step = 100] loss: 0.245, accuracy: 0.909
[step = 200] loss: 0.240, accuracy: 0.918
EPOCH = 19, loss = 0.234,accuracy = 0.919, val_loss = 0.244, val_accuracy = 0.908
================================================================================2020-05-03 00:07:25
[step = 100] loss: 0.246, accuracy: 0.904
[step = 200] loss: 0.232, accuracy: 0.912
EPOCH = 20, loss = 0.226,accuracy = 0.917, val_loss = 0.234, val_accuracy = 0.922
================================================================================2020-05-03 00:07:26
Finished Training...
验证模型
# 结果可视化
fig, (ax1,ax2) = plt.subplots(nrows=1,ncols=2,figsize = (12,5))
ax1.scatter(Xp[:,0],Xp[:,1], c="r")
ax1.scatter(Xn[:,0],Xn[:,1],c = "g")
ax1.legend(["positive","negative"]);
ax1.set_title("y_true");
Xp_pred = X[torch.squeeze(net.forward(X)>=0.5)]
Xn_pred = X[torch.squeeze(net.forward(X)<0.5)]
ax2.scatter(Xp_pred[:,0],Xp_pred[:,1],c = "r")
ax2.scatter(Xn_pred[:,0],Xn_pred[:,1],c = "g")
ax2.legend(["positive","negative"]);
ax2.set_title("y_pred")
plt.show()
%matplotlib inline
%config InlineBackend.figure_format = 'svg'
import matplotlib.pyplot as plt
def plot_metric(dfhistory, metric):
train_metrics = dfhistory[metric]
val_metrics = dfhistory['val_'+metric]
epochs = range(1, len(train_metrics) + 1)
plt.plot(epochs, train_metrics, 'bo--')
plt.plot(epochs, val_metrics, 'ro-')
plt.title('Training and validation '+ metric)
plt.xlabel("Epochs")
plt.ylabel(metric)
plt.legend(["train_"+metric, 'val_'+metric])
plt.show()
plot_metric(dfhistory,"loss")
plot_metric(dfhistory,"accuracy")
使用模型
def predict(model,dl):
model.eval()
result = torch.cat([model.forward(t[0]) for t in dl])
return(result.data)
#预测概率
y_pred_probs = predict(net,dl_valid)
y_pred_probs
tensor([[0.9995],
[0.9979],
[0.3963],
...,
[0.9828],
[0.9479],
[0.3365]])
#预测类别
y_pred = torch.where(y_pred_probs>0.5,
torch.ones_like(y_pred_probs),torch.zeros_like(y_pred_probs))
y_pred
tensor([[0.],
[1.],
[0.],
...,
[0.],
[1.],
[0.]])
保存模型
推荐使用保存参数方式保存 PyTorch 模型。
print(net.state_dict().keys())
odict_keys(['fc1.weight', 'fc1.bias', 'fc2.weight', 'fc2.bias', 'fc3.weight', 'fc3.bias'])
# 保存模型参数
torch.save(net.state_dict(), "./data/net_parameter.pkl")
net_clone = Net()
net_clone.load_state_dict(torch.load("./data/net_parameter.pkl"))
predict(net_clone,dl_valid)
tensor([[0.4916],
[0.9088],
[0.0243],
...,
[0.2110],
[0.8611],
[0.4693]])
小结
以上就是使用 TensorFlow 2 和 PyTorch 炼丹的基本套路，大家可以从中感受炼丹的乐趣。基本的思路都是：
- 准备数据
- 定义模型
- 训练模型
- 评估模型
- 使用模型
- 保存模型