Keras: Binary_crossentropy has negative values : <p>I'm following <a href="http://machinelearningmastery.com/tutorial-first-neural-network-python-keras/" rel="noreferrer">this tutorial</a> (section 6: Tying it All Together), with my own dataset. I can get the example in the tutorial working, no problem, with the sample dataset provided.</p>

<p>I'm getting a binary cross-entropy error that is negative, and no improvements as epochs progress. I'm pretty sure binary cross-entropy should always be positive, and I should see some improvement in the loss. I've truncated the sample output (and code call) below to 5 epochs. Others seem to run into similar problems sometimes when training CNNs, but I didn't see a clear solution in my case. Does anyone know why this is happening?</p>

<p>Sample output:</p>

<pre><code>Creating TensorFlow device (/gpu:2) -&gt; (device: 2, name: GeForce GTX TITAN Black, pci bus id: 0000:84:00.0)
10240/10240 [==============================] - 2s - loss: -5.5378 - acc: 0.5000 - val_loss: -7.9712 - val_acc: 0.5000
Epoch 2/5
10240/10240 [==============================] - 0s - loss: -7.9712 - acc: 0.5000 - val_loss: -7.9712 - val_acc: 0.5000
Epoch 3/5
10240/10240 [==============================] - 0s - loss: -7.9712 - acc: 0.5000 - val_loss: -7.9712 - val_acc: 0.5000
Epoch 4/5
10240/10240 [==============================] - 0s - loss: -7.9712 - acc: 0.5000 - val_loss: -7.9712 - val_acc: 0.5000
Epoch 5/5
10240/10240 [==============================] - 0s - loss: -7.9712 - acc: 0.5000 - val_loss: -7.9712 - val_acc: 0.5000
</code></pre>

<p>My code:</p>

<pre><code>import numpy as np
import keras
from keras.models import Sequential
from keras.layers import Dense
from keras.callbacks import History

history = History()
seed = 7
np.random.seed(seed)

dataset = np.loadtxt('train_rows.csv', delimiter=",")

#print dataset.shape (10240, 64)

# split into input (X) and output (Y) variables
X = dataset[:, 0:(dataset.shape[1]-2)] #0:62 (63 of 64 columns)
Y = dataset[:, dataset.shape[1]-1]  #column 64 counting from 0

#print X.shape (10240, 62)
#print Y.shape (10240,)

testset = np.loadtxt('test_rows.csv', delimiter=",")

#print testset.shape (2560, 64)

X_test = testset[:,0:(testset.shape[1]-2)]
Y_test = testset[:,testset.shape[1]-1]

#print X_test.shape (2560, 62)
#print Y_test.shape (2560,)

num_units_per_layer = [100, 50]

### create model
model = Sequential()
model.add(Dense(100, input_dim=(dataset.shape[1]-2), init='uniform', activation='relu'))
model.add(Dense(50, init='uniform', activation='relu'))
model.add(Dense(1, init='uniform', activation='sigmoid'))

model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
## Fit the model
model.fit(X, Y, validation_data=(X_test, Y_test), nb_epoch=5, batch_size=128)
</code></pre>
