What is a Neural Network in TensorFlow?
Neural networks are a type of machine learning algorithm that are modeled after the human brain. They are used to solve complex problems and can be used in a wide range of applications, from image recognition to natural language processing. TensorFlow is an open-source software library for numerical computation. It is used for dataflow programming and machine learning applications. In this article, we will be discussing how to use TensorFlow to create a neural network.
Creating Input, Target, and Weight Variables in TensorFlow
The first step in creating a neural network in TensorFlow is to create input, target, and weight variables. The input variable, X, should be a placeholder of type float32 and should have a shape of (None, D). The target variable, T, should also be a placeholder of type float32 and should have a shape of (None, K). The weight variables, W1 and W2, should be of type float32 and should be initialized with the given values.
Calculating the Output in TensorFlow
Once the input and weight variables have been created, the next step is to calculate the output. This is done by first taking the dot product of X and W1, followed by applying a ReLU activation function to the result. This will give us the hidden layer output, which is then multiplied by W2 to give us the output of the neural network, Yish.
Cost Function in TensorFlow
Once the output of the neural network has been calculated, the next step is to compute the cost function. This is done using the tf.reduce_sum() function, which takes the softmax cross entropy of Yish and T, and returns the sum of the losses.
Creating Train and Predict Functions in TensorFlow
The next step is to create train and predict functions. The train function should be an RMSPropOptimizer that is used to minimize the cost function. The predict function should be an argmax of Yish, with an axis of 1.
Initializing Variables in TensorFlow
Before the neural network can be trained, all of the variables need to be initialized. This is done using the tf.initialize_all_variables() function.
Training and Predicting with TensorFlow
Once the variables have been initialized, the neural network can be trained and predicted. This is done by running a loop over the max_iter and n_batches variables and running the train and predict functions in the loop. The train function should use the Xbatch and Ybatch variables, while the predict function should use the Xtest and Ytest_ind variables.
Exercise: Running the Neural Network on the MNIST Dataset
Now that you have a basic understanding of how to create a neural network in TensorFlow, let’s try running it on the MNIST dataset.
Create a 1-hidden layer neural network with 500, 1000, 2000, and 3000 hidden units. What is the impact on training error and test error?
When running a 1-hidden layer neural network on the MNIST dataset with 500, 1000, 2000, and 3000 hidden units, the training error and test error decrease as the number of hidden units increases. This is because the more hidden units, the more complex the model can become, allowing it to better fit the data.
Create neural networks with 1, 2, and 3 hidden layers, all with 500 hidden units. What is the impact on training error and test error? (Hint: It should be overfitting when you have too many hidden layers).
When running a neural network on the MNIST dataset with 1, 2, and 3 hidden layers, all with 500 hidden units, the training error and test error decrease as the number of hidden layers increases. However, when the number of hidden layers is too high, the model will start to overfit the data. This means that the training error will continue to decrease, while the test error will start to increase. This is due to the model becoming too complex and learning patterns that are specific to the training data and not general enough to be applied to the test data.