Keras: Binary_crossentropy has negative values

Question

I am following this lesson (section 6: How to relate all this) to my own dataset. I can get an example from the tutorial, without any problems with the provided sample dataset.

I get a binary cross-entropy error that is negative, and no improvement as ages evolve. I am pretty sure binary cross-entropy should always be positive, and I should see some improvement in loss. I shortened the output example (and code call) below to 5 eras. Others seem to encounter similar problems sometimes when learning CNN, but I did not see a clear solution in my case. Does anyone know why this is happening?

Output Example:

Creating TensorFlow device (/gpu:2) -> (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

My code is:

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)

Answer 1

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Answer 2

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