如何在Python中使用TensorFlow定义损失函数、优化器、训练模型并在IMDB数据集上进行评估？

TensorFlow是Google提供的机器学习框架。它是一个开源框架，与Python一起用于实现算法、深度学习应用等。它被用于研究和生产目的。

可以使用以下代码在Windows上安装’tensorflow’软件包 –

pip install tensorflow

‘IMDB’数据集包含超过50,000部电影的评论。此数据集通常与自然语言处理相关的操作一起使用。

我们正在使用Google Colaboratory来运行以下代码。Google Colab或Colaboratory可以在浏览器上运行Python代码，并且不需要配置，并且可以免费访问GPU（图形处理单元）。Colaboratory是在Jupyter Notebook上构建的。

以下是定义损失函数、优化器、训练模型并在IMDB数据集上进行评估的代码片段 –

model.compile(loss=losses.BinaryCrossentropy(from_logits=True),
              optimizer='adam',
              metrics=tf.metrics.BinaryAccuracy(threshold=0.0))
epochs = 10
history = model.fit(
    train_ds,
    validation_data=val_ds,
    epochs=epochs)
loss, accuracy = model.evaluate(test_ds)

print("Loss is : ", loss)
print("Accuracy is : ", accuracy)

代码来源 - https://www.tensorflow.org/tutorials/keras/text_classification

更多Python相关文章，请阅读：Python 教程

输出

Epoch 1/10
625/625 [==============================] - 12s 19ms/step - loss: 0.6818 - binary_accuracy: 0.6130 - val_loss: 0.6135 - val_binary_accuracy: 0.7750
Epoch 2/10
625/625 [==============================] - 4s 7ms/step - loss: 0.5785 - binary_accuracy: 0.7853 - val_loss: 0.4971 - val_binary_accuracy: 0.8230
Epoch 3/10
625/625 [==============================] - 4s 7ms/step - loss: 0.4651 - binary_accuracy: 0.8372 - val_loss: 0.4193 - val_binary_accuracy: 0.8470
Epoch 4/10
625/625 [==============================] - 4s 7ms/step - loss: 0.3901 - binary_accuracy: 0.8635 - val_loss: 0.3732 - val_binary_accuracy: 0.8612
Epoch 5/10
625/625 [==============================] - 4s 7ms/step - loss: 0.3435 - binary_accuracy: 0.8771 - val_loss: 0.3444 - val_binary_accuracy: 0.8688
Epoch 6/10
625/625 [==============================] - 4s 7ms/step - loss: 0.3106 - binary_accuracy: 0.8877 - val_loss: 0.3255 - val_binary_accuracy: 0.8730
Epoch 7/10
625/625 [==============================] - 5s 7ms/step - loss: 0.2855 - binary_accuracy: 0.8970 - val_loss: 0.3119 - val_binary_accuracy: 0.8732
Epoch 8/10
625/625 [==============================] - 5s 7ms/step - loss: 0.2652 - binary_accuracy: 0.9048 - val_loss: 0.3027 - val_binary_accuracy: 0.8772
Epoch 9/10
625/625 [==============================] - 5s 7ms/step - loss: 0.2481 - binary_accuracy: 0.9125 - val_loss: 0.2959 - val_binary_accuracy: 0.8782
Epoch 10/10
625/625 [==============================] - 5s 7ms/step - loss: 0.2328 - binary_accuracy: 0.9161 - val_loss: 0.2913 - val_binary_accuracy: 0.8792
782/782 [==============================] - 10s 12ms/step - loss: 0.3099 - binary_accuracy: 0.8741
Loss is : 0.3099007308483124
Accuracy is : 0.8741199970245361