spike checker

spikechecker.py

# conv1d for timeseries spike
model = models.Sequential()

model.add(layers.Conv1D(filters = 1,
                        kernel_size = 10,
                        activation = 'relu',
                        input_shape=(timesteps,1)))
model.add(layers.GlobalMaxPooling1D())
model.add(layers.Flatten())
model.add(layers.Dense(1, activation = 'sigmoid'))
model.compile(optimizer = 'rmsprop',
              loss = 'binary_crossentropy',
              metrics = ['accuracy'])

network = model.fit(x, y[:,0],
                    epochs = 20,
                    batch_size = 32,
                    shuffle = True)