How to define a multi-dimensional neural network with keras
If you want to use a simple neural network which takes in a 50x2 matrix you can do this y flattening your matrix to a 100x1 vector. This will make no difference in the way that the neurons are processed. Each neuron will take all the inputs and make a decision on its weight accordingly. 

If you want some neighborhood feature selection then you can use a convolutional neural network like

model = Sequential()
model.add(Conv2D(32, kernel_size=(2, 2),
                 activation='relu',
                 input_shape=input_shape,
                 padding='same'))
model.add(Conv2D(64, (2, 2), activation='relu', padding='same'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(1, activation='softmax'))

model.compile(loss='binary_crossentropy', 
              optimizer='adam', 
              metrics=['accuracy'])


This will do a feature mixing within a 2x2 window across your matrix which will thus mix your two values before feeding them to the deeper part of the network. 

Doing this is also a good idea as it serves as a means to reduce the complexity of your model which may lead to better results.