Model Training and Evaluation
Now we are ready to train our model.
Python3
model.fit(X_train, Y_train, epochs=5, batch_size=64, verbose=1, validation_data=(X_val, Y_val)) |
Output:
Epoch 1/5 428/428 [==============================] - 5s 8ms/step - loss: 1.8563 - auc: 0.6886 - val_loss: 1.6245 - val_auc: 0.7530 Epoch 2/5 428/428 [==============================] - 3s 7ms/step - loss: 1.6319 - auc: 0.7554 - val_loss: 1.5624 - val_auc: 0.7769 Epoch 3/5 428/428 [==============================] - 4s 8ms/step - loss: 1.5399 - auc: 0.7845 - val_loss: 1.5510 - val_auc: 0.7814 Epoch 4/5 428/428 [==============================] - 5s 11ms/step - loss: 1.4883 - auc: 0.7999 - val_loss: 1.5106 - val_auc: 0.7929 Epoch 5/5 428/428 [==============================] - 3s 8ms/step - loss: 1.4408 - auc: 0.8146 - val_loss: 1.4992 - val_auc: 0.7971
By using this neural network with two hidden layers we have achieved a 0.8 AUC-ROC score which implies that the predictions made will be around 80% accurate.
Python3
results = model.evaluate(X_val, Y_val, verbose=0) print('Validation loss :', results[0]) print('Validation Accuracy :', results[1]) |
Output:
Validation loss : 1.4992401599884033 Validation Accuracy : 0.7971429824829102
Hidden Layer Perceptron in TensorFlow
In this article, we will learn about hidden layer perceptron. A hidden layer perceptron is nothing but a hi-fi terminology for a neural network with one or more hidden layers. The purpose which is being served by these hidden layers is that they help to learn complex and non-linear functions for a task.
Hidden Layer Perceptron in TensorFlow
The above image is the simplest representation of the hidden layer perceptron with a single hidden layer. Here we can see that the input for the final layer is the neurons of the hidden layers. So, in a hidden layer perceptron network input for the current layer is the output of the previous layer.
We will try to understand how one can implement a Hidden layer perceptron network using TensorFlow. Also, the data used for this purpose is the famous Facial Recognition dataset.
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