Number of neurons in input layer for Feedforward neural network

Question

I'm trying to classify 1D data with 3-layered feedforward neural network (multilayer perceptron).

Currently I have input samples (time-series) consisting of 50 data points each. I've read on many sources that number of neurons in input layer should be equal to number of data points (50 in my case), however, after experimenting with cross validation a bit, I've found that I can get slightly better average classification (with lover variation as well) performance with 25 neurons in input layer.

I'm trying to understand math behind it: does it makes any sense to have lower number of neurons than data points in input layer? Or maybe results are better just because of some errors?

Also - are there any other rules to set number of neurons in input layer?

Update - to clarify what I mean:

I use Keras w tensorflow backend for this. My model looks like this:

model = Sequential()
model.add(Dense(25, input_dim=50, activation='relu'))
model.add(Dense(8, activation='relu'))
model.add(Dense(1, activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(input_data, output_data, epochs=150, batch_size=10)

predictions = model.predict(X)
rounded = [round(x[0]) for x in predictions]
print(rounded)

input_data, output_data - numpy arrays with my data points in former and corresponding value of 1 or 0 in latter.

25 is number of neurons in first layer and input_dim is number of my data points, therefore technically it works, yet I'm not sure whether it makes sense to do so or I misunderstood concept of neurons in input layer and what they do.