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How can TensorFlow be used to define a loss function, an optimizer, train the model and evaluate it on the IMDB dataset in Python?
Tensorflow is a machine learning framework that is provided by Google. It is an open-source framework used in conjunction with Python to implement algorithms, deep learning applications and much more. It is used in research and for production purposes.
The ‘tensorflow’ package can be installed on Windows using the below line of code −
pip install tensorflow
The ‘IMDB’ dataset contains reviews of over 50 thousand movies. This dataset is generally used with operations associated with Natural Language Processing.
We are using the Google Colaboratory to run the below code. Google Colab or Colaboratory helps run Python code over the browser and requires zero configuration and free access to GPUs (Graphical Processing Units). Colaboratory has been built on top of Jupyter Notebook.
Following is the code snippet to define a loss function, an optimizer, train the model and evaluate it on the IMDB dataset −
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)Code credit − https://www.tensorflow.org/tutorials/keras/text_classification
Output
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
Explanation
Once the model has been built, it is compiled using the ‘compile’ function.
The number of steps defined to train the model is 10 here.
The ‘fit’ function is used to fit the data to the model that was built.
The ‘evaluate’ function is used to compute the loss and the accuracy of the model on the test dataset.
The values of loss and accuracy are displayed on the console.
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