Classification with TF

First, we shall start with a very simple deep neural network, one containing only a single FC hidden layer (with ReLU activation) and a softmax FC layer, with no convolutional layer. The next screenshot shows the network upside down. The input is a flattened image containing 28 x 28 nodes and 1,024 nodes in the hidden layer and 10 output nodes, corresponding to each of the digits to be classified:

Now let's implement the deep learning image classification with TF. First, we need to load the mnist dataset and divide the training images into two parts, the first one being the larger (we use 50k images) for training, and ...

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