Keras: X and Y are the same, yet validation accuracy is 50%, what is wrong? I am trying to understand what is going on so I built a simpler version of my project. I set the X and the Y to be identical and I'm trying to predict Y using X, this should be very simple, but my setup isn't working. Here is my code : 

import numpy
import keras
import pandas


# I want to evaluate the model when X and Y are the same
# This should be very easy for the model to evaluate
X = numpy.random.randint(2, size=100000)
Y = X

# Setup the model
model = keras.models.Sequential()

model.add(keras.layers.Dense(1, input_dim=1, init='uniform', activation='relu'   ))
model.add(keras.layers.Dense(1,              init='uniform', activation='sigmoid'))

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

hist = model.fit(X, Y, nb_epoch=10, validation_split=.3, batch_size=10, verbose=1)

df             = pandas.DataFrame()
df['loss']     = hist.history['loss']
df['acc']      = hist.history['acc']
df['val_loss'] = hist.history['val_loss']
df['val_acc']  = hist.history['val_acc']
df.index       = df.index + 1
#
print(df)


And this is my output: 

        loss       acc  val_loss   val_acc
1   0.693162  0.504357  0.693300  0.496233
2   0.693150  0.503100  0.693250  0.496233
3   0.693157  0.502357  0.693132  0.503767
4   0.693171  0.502214  0.693119  0.503767
5   0.693167  0.502043  0.693121  0.503767
6   0.693129  0.504014  0.693133  0.503767
7   0.693167  0.503243  0.693129  0.503767
8   0.693157  0.502357  0.693181  0.496233
9   0.693180  0.502614  0.693141  0.503767
10  0.693170  0.502300  0.693119  0.503767


I expected that accuracy to go to 100%, but that is not the case. What am I doing wrong?

This is the example that I was following.
You should think about how the initial values impact the ReL Units. If, for example, you use init='one' for the activation='relu' layer you'll get the desired result (in this simple setup).