How to use the black/white image as the input to tensorflow

You can directly load the Image data as you would normally do, the Image being binary will have no effect other that the input channel width becoming 1 for the input.

Whenever you put an Image through a convnet, each output filter generally learns features for all the channels, so in case of a binary image, there is a separate kernel defined for each input channel / output channel combination (Since Only 1 input channel) in the first layer.

Each channel is defined by it's number of filters and there exists a 2D kernel for each input channel which averages over all filters, so you will have weights/parameters equal to input_channels * number_of_filters * filter_dims, here for the first layer input_channels becomes one.

Since you asked for some sample code. Let your image be in a tensor X, simply use

X_out = tf.nn.conv2d(X, filters = 6, kernel_size = [height,width])

After that you can apply an activation, this will make your output image have 6 channels. If you face any problem or have some doubts, feel free to comment, for theoretical clarification, check out https://www.coursera.org/learn/convolutional-neural-networks/lecture/nsiuW/one-layer-of-a-convolutional-network

Edit

Since the question was about simple neural net, not conv net, here is the code for that,

X_train is the variable in which image is stored as (n_x,n_x) byte resolution, n_x is used later.

You will need to flatten the input.

X_train_flatten = X_train_orig.reshape(X_train_orig.shape[0], -1).T This first flattens the image horizontally and then transposes it to arrange it vertically.

Then you will create placeholder tensor X as :

X = tf.placeholder(tf.bool,[n_x*n_x,None]) #Your Input tensor should have dimension same as your input layer.

let W, b be weight and bias respectively.

Z1 = tf.add(tf.matmul(W1,X),b1)  #Linear Transformation step
A1 = tf.nn.relu(Z1)              #Activation Step

And you keep on creating your graph, I think that answers your question, if not let me know.