Keras prediction class labeling - keras

Question

I need help to understand Keras prediction class labeling
I am building the binary classification model with Keras. I use 3 'relu' layers plus sigmoid output, the loss function is binary_crossentropy.
The point is that I am not able to understand simple thing:
- for my y_train/y_test I use two values: False / True. When I predict on the X_test I am getting he probability or class predicted, fine. But unfortunately the model has in 50% of cases the accuracy of 0,1 and in other 50% of cases 0,9. This comes from the fact that the distribution of the False / True is uneven - 90% of False and 10% of True. ... and then, what happens is that it looks like the class label for False is 0 in half of the cases and 1 in other half of cases. I am unable to figure out why.
I found some posts saying Keras would number the classes in alphabetical order, so I assumed False would always get 0 and True 1, but this does not seem to be the case.
Please find my code below and advise what I do wrong.
# ####################### Fetch data #################################
#df = pd.read_csv(os.path.join(data_folder, data_file))
df = pd.read_csv('\\\...\\train_data_with_anomalous_column.csv', delimiter=',')
x = df.drop('ANOMALOUS', axis=1)
# x = x.drop('TIMESTAMP', axis=1)
y = df['ANOMALOUS'].copy()
x = x.as_matrix()
y = y.as_matrix()
# y.astype(int)
X_train, X_test, y_train, y_test = train_test_split(x, y, test_size=0.1)
# ####################### get rid of TIMESTAMP columns ###############
# ###### first, copy the TIMESTAMP column into ananother np-array
# ###### in order to be able to join it later
X_test_TST = X_test[:, [0]]
X_train = np.delete(X_train, 0, 1)
X_test = np.delete(X_test, 0, 1)
class_weight = {False: 1.,
True: 1.
}
training_set_size = X_train.shape[0]
n_inputs = 22
n_h1 = args.n_hidden_1
n_h2 = args.n_hidden_2
n_h3 = args.n_hidden_3
n_outputs = 1
n_epochs = 2
model_validation_split = 0.1
batch_size = args.batch_size
learning_rate = args.learning_rate
print(X_train.shape, y_train.shape, X_test.shape, y_test.shape, sep='\n')
# Build a simple MLP model
model = Sequential()
# first hidden layer
model.add(Dense(n_h1, activation='relu', input_shape=(n_inputs,)))
# second hidden layer
model.add(Dense(n_h2, activation='relu'))
# second hidden layer
model.add(Dense(n_h3, activation='relu'))
# output layer
model.add(Dense(n_outputs, activation='sigmoid'))
model.summary()
model.compile(loss='binary_crossentropy',
optimizer=optimizer,
metrics=['accuracy']
)
# start an Azure ML run
run = Run.get_context()
class LogRunMetrics(Callback):
# callback at the end of every epoch
def on_epoch_end(self, epoch, log):
# log a value repeated which creates a list
run.log('Loss', log['loss'])
run.log('Accuracy', log['acc'])
history = model.fit(X_train, y_train,
batch_size=batch_size,
epochs=n_epochs,
verbose=2,
class_weight=class_weight,
callbacks=[LogRunMetrics()])
score = model.evaluate(X_test, y_test, verbose=1)
# #### get accuracy, F1, precision and recall
# Get the class predictions
y_classes = model.predict_classes(X_test, batch_size=batch_size, verbose=1)
print(y_classes
print(classification_report(y_test, y_classes))
...