如何使用 Tensorflow 与 Estimator 来使用 Python 编译模型?
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Tensorflow 可以与 Estimator 一起使用,在‘train’ 的帮助下编译模型方法。
阅读更多: 什么是 TensorFlow,以及 Keras 如何与 TensorFlow 配合使用来创建神经网络?
我们将使用 Keras Sequential API,它有助于构建用于处理普通层堆栈的顺序模型,其中每个层都有一个输入张量和一个输出张量。
包含至少一个层的神经网络称为卷积层。我们可以使用卷积神经网络构建学习模型。
TensorFlow Text 包含可与 TensorFlow 2.0 一起使用的文本相关类和操作的集合。TensorFlow Text 可用于预处理序列建模。
我们正在使用 Google Colaboratory 运行以下代码。 Google Colab 或 Colaboratory 可帮助在浏览器上运行 Python 代码,无需配置,可免费访问 GPU(图形处理单元)。Colaboratory 建立在 Jupyter Notebook 之上。
Estimator 是 TensorFlow 对完整模型的高级表示。它旨在轻松扩展和异步训练。
该模型使用鸢尾花数据集进行训练。有 4 个特征和一个标签。
- 萼片长度
- 萼片宽度
- 花瓣长度
- 花瓣宽度
示例
print("The model is being trained") classifier.train(input_fn=lambda: input_fn(train, train_y, training=True), steps=5000)
代码来源 −https://www.tensorflow.org/tutorials/estimator/premade#first_things_first
输出
WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/training/training_util.py:236: Variable.initialized_value (from tensorflow.python.ops.variables) is deprecated and will be removed in a future version. Instructions for updating: Use Variable.read_value. Variables in 2.X are initialized automatically both in eager and graph (inside tf.defun) contexts. INFO:tensorflow:Calling model_fn. WARNING:tensorflow:Layer dnn is casting an input tensor from dtype float64 to the layer's dtype of float32, which is new behavior in TensorFlow 2. The layer has dtype float32 because its dtype defaults to floatx. If you intended to run this layer in float32, you can safely ignore this warning. If in doubt, this warning is likely only an issue if you are porting a TensorFlow 1.X model to TensorFlow 2. To change all layers to have dtype float64 by default, call `tf.keras.backend.set_floatx('float64')`. To change just this layer, pass dtype='float64' to the layer constructor. If you are the author of this layer, you can disable autocasting by passing autocast=False to the base Layer constructor. WARNING:tensorflow:From /tmpfs/src/tf_docs_env/lib/python3.6/site-packages/tensorflow/python/keras/optimizer_v2/adagrad.py:83: calling Constant.__init__ (from tensorflow.python.ops.init_ops) with dtype is deprecated and will be removed in a future version. Instructions for updating: Call initializer instance with the dtype argument instead of passing it to the constructor INFO:tensorflow:Done calling model_fn. INFO:tensorflow:Create CheckpointSaverHook. INFO:tensorflow:Graph was finalized. INFO:tensorflow:Running local_init_op. INFO:tensorflow:Done running local_init_op. INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 0... INFO:tensorflow:Saving checkpoints for 0 into /tmp/tmpbhg2uvbr/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 0... INFO:tensorflow:loss = 1.1140382, step = 0 INFO:tensorflow:global_step/sec: 312.415 INFO:tensorflow:loss = 0.8781501, step = 100 (0.321 sec) INFO:tensorflow:global_step/sec: 375.535 INFO:tensorflow:loss = 0.80712265, step = 200 (0.266 sec) INFO:tensorflow:global_step/sec: 372.712 INFO:tensorflow:loss = 0.7615077, step = 300 (0.268 sec) INFO:tensorflow:global_step/sec: 368.782 INFO:tensorflow:loss = 0.733555, step = 400 (0.271 sec) INFO:tensorflow:global_step/sec: 372.689 INFO:tensorflow:loss = 0.6983943, step = 500 (0.268 sec) INFO:tensorflow:global_step/sec: 370.308 INFO:tensorflow:loss = 0.67940104, step = 600 (0.270 sec) INFO:tensorflow:global_step/sec: 373.374 INFO:tensorflow:loss = 0.65386146, step = 700 (0.268 sec) INFO:tensorflow:global_step/sec: 368.335 INFO:tensorflow:loss = 0.63730353, step = 800 (0.272 sec) INFO:tensorflow:global_step/sec: 371.575 INFO:tensorflow:loss = 0.61313766, step = 900 (0.269 sec) INFO:tensorflow:global_step/sec: 371.975 INFO:tensorflow:loss = 0.6123625, step = 1000 (0.269 sec) INFO:tensorflow:global_step/sec: 369.615 INFO:tensorflow:loss = 0.5957534, step = 1100 (0.270 sec) INFO:tensorflow:global_step/sec: 374.054 INFO:tensorflow:loss = 0.57203, step = 1200 (0.267 sec) INFO:tensorflow:global_step/sec: 369.713 INFO:tensorflow:loss = 0.56556034, step = 1300 (0.270 sec) INFO:tensorflow:global_step/sec: 366.202 INFO:tensorflow:loss = 0.547443, step = 1400 (0.273 sec) INFO:tensorflow:global_step/sec: 361.407 INFO:tensorflow:loss = 0.53326523, step = 1500 (0.277 sec) INFO:tensorflow:global_step/sec: 367.461 INFO:tensorflow:loss = 0.51837724, step = 1600 (0.272 sec) INFO:tensorflow:global_step/sec: 364.181 INFO:tensorflow:loss = 0.5281174, step = 1700 (0.275 sec) INFO:tensorflow:global_step/sec: 368.139 INFO:tensorflow:loss = 0.5139683, step = 1800 (0.271 sec) INFO:tensorflow:global_step/sec: 366.277 INFO:tensorflow:loss = 0.51073176, step = 1900 (0.273 sec) INFO:tensorflow:global_step/sec: 366.634 INFO:tensorflow:loss = 0.4949246, step = 2000 (0.273 sec) INFO:tensorflow:global_step/sec: 364.732 INFO:tensorflow:loss = 0.49381495, step = 2100 (0.274 sec) INFO:tensorflow:global_step/sec: 365.006 INFO:tensorflow:loss = 0.48916715, step = 2200 (0.274 sec) INFO:tensorflow:global_step/sec: 366.902 INFO:tensorflow:loss = 0.48790723, step = 2300 (0.273 sec) INFO:tensorflow:global_step/sec: 362.232 INFO:tensorflow:loss = 0.47671652, step = 2400 (0.276 sec) INFO:tensorflow:global_step/sec: 368.592 INFO:tensorflow:loss = 0.47324088, step = 2500 (0.271 sec) INFO:tensorflow:global_step/sec: 371.611 INFO:tensorflow:loss = 0.46822113, step = 2600 (0.269 sec) INFO:tensorflow:global_step/sec: 362.345 INFO:tensorflow:loss = 0.4621966, step = 2700 (0.276 sec) INFO:tensorflow:global_step/sec: 362.788 INFO:tensorflow:loss = 0.47817266, step = 2800 (0.275 sec) INFO:tensorflow:global_step/sec: 368.473 INFO:tensorflow:loss = 0.45853442, step = 2900 (0.271 sec) INFO:tensorflow:global_step/sec: 360.944 INFO:tensorflow:loss = 0.44062576, step = 3000 (0.277 sec) INFO:tensorflow:global_step/sec: 370.982 INFO:tensorflow:loss = 0.4331399, step = 3100 (0.269 sec) INFO:tensorflow:global_step/sec: 366.248 INFO:tensorflow:loss = 0.45120597, step = 3200 (0.273 sec) INFO:tensorflow:global_step/sec: 371.703 INFO:tensorflow:loss = 0.4403404, step = 3300 (0.269 sec) INFO:tensorflow:global_step/sec: 362.176 INFO:tensorflow:loss = 0.42405623, step = 3400 (0.276 sec) INFO:tensorflow:global_step/sec: 363.283 INFO:tensorflow:loss = 0.41672814, step = 3500 (0.275 sec) INFO:tensorflow:global_step/sec: 363.529 INFO:tensorflow:loss = 0.42626005, step = 3600 (0.275 sec) INFO:tensorflow:global_step/sec: 367.348 INFO:tensorflow:loss = 0.4089098, step = 3700 (0.272 sec) INFO:tensorflow:global_step/sec: 363.067 INFO:tensorflow:loss = 0.41276374, step = 3800 (0.275 sec) INFO:tensorflow:global_step/sec: 364.771 INFO:tensorflow:loss = 0.4112524, step = 3900 (0.274 sec) INFO:tensorflow:global_step/sec: 363.167 INFO:tensorflow:loss = 0.39261794, step = 4000 (0.275 sec) INFO:tensorflow:global_step/sec: 362.082 INFO:tensorflow:loss = 0.41160905, step = 4100 (0.276 sec) INFO:tensorflow:global_step/sec: 364.979 INFO:tensorflow:loss = 0.39620766, step = 4200 (0.274 sec) INFO:tensorflow:global_step/sec: 363.323 INFO:tensorflow:loss = 0.39696264, step = 4300 (0.275 sec) INFO:tensorflow:global_step/sec: 361.25 INFO:tensorflow:loss = 0.38196522, step = 4400 (0.277 sec) INFO:tensorflow:global_step/sec: 365.666 INFO:tensorflow:loss = 0.38667366, step = 4500 (0.274 sec) INFO:tensorflow:global_step/sec: 361.202 INFO:tensorflow:loss = 0.38149032, step = 4600 (0.277 sec) INFO:tensorflow:global_step/sec: 365.038 INFO:tensorflow:loss = 0.37832782, step = 4700 (0.274 sec) INFO:tensorflow:global_step/sec: 366.375 INFO:tensorflow:loss = 0.3726803, step = 4800 (0.273 sec) INFO:tensorflow:global_step/sec: 366.474 INFO:tensorflow:loss = 0.37167495, step = 4900 (0.273 sec) INFO:tensorflow:Calling checkpoint listeners before saving checkpoint 5000... INFO:tensorflow:Saving checkpoints for 5000 into /tmp/tmpbhg2uvbr/model.ckpt. INFO:tensorflow:Calling checkpoint listeners after saving checkpoint 5000... INFO:tensorflow:Loss for final step: 0.36297452. <tensorflow_estimator.python.estimator.canned.dnn.DNNClassifierV2 at 0x7fc9983ed470>
解释
- 一旦创建了 Estimator 对象,就可以调用以下方法 −
- 模型已训练。
- 已训练的模型经过评估。
- 此模型用于进行预测。
- 模型再次经过训练。
- 通过调用 Estimator 的 train 方法完成。