NaN loss when training regression network
<p>I have a data matrix in "one-hot encoding" (all ones and zeros) with 260,000 rows and 35 columns.  I am using Keras to train a simple neural network to predict a continuous variable.  The code to make the network is the following:</p>

<pre><code>model = Sequential()
model.add(Dense(1024, input_shape=(n_train,)))
model.add(Activation('relu'))
model.add(Dropout(0.1))

model.add(Dense(512))
model.add(Activation('relu'))
model.add(Dropout(0.1))

model.add(Dense(256))
model.add(Activation('relu'))
model.add(Dropout(0.1))
model.add(Dense(1))

sgd = SGD(lr=0.01, nesterov=True);
#rms = RMSprop()
#model.compile(loss='categorical_crossentropy', optimizer=rms, metrics=['accuracy'])
model.compile(loss='mean_absolute_error', optimizer=sgd)
model.fit(X_train, Y_train, batch_size=32, nb_epoch=3, verbose=1, validation_data=(X_test,Y_test), callbacks=[EarlyStopping(monitor='val_loss', patience=4)] )
</code></pre>

<p>However, during the training process, I see the loss decrease nicely, but during the middle of the second epoch, it goes to nan:</p>

<pre><code>Train on 260000 samples, validate on 64905 samples
Epoch 1/3
260000/260000 [==============================] - 254s - loss: 16.2775 - val_loss:
 13.4925
Epoch 2/3
 88448/260000 [=========&gt;....................] - ETA: 161s - loss: nan
</code></pre>

<p>I tried using <code>RMSProp</code> instead of <code>SGD</code>, I tried <code>tanh</code> instead of <code>relu</code>, I tried with and without dropout, all to no avail.  I tried with a smaller model, i.e. with only one hidden layer, and same issue (it becomes nan at a different point).  However, it does work with less features, i.e. if there are only 5 columns, and gives quite good predictions. It seems to be there is some kind of overflow, but I can't imagine why--the loss is not unreasonably large at all.  </p>

<p>Python version 2.7.11, running on a linux machine, CPU only.  I tested it with the latest version of Theano, and I also get Nans, so I tried going to Theano 0.8.2 and have the same problem.  With the latest version of Keras has the same problem, and also with the 0.3.2 version.  </p>