def create_model():
LENGTH = 5000 # Packet sequence length
INPUT_SHAPE = (LENGTH, 1)
NB_CLASSES = 95 # number of outputs = number of classes
OPTIMIZER = Adamax(lr=0.002,
beta_1=0.9,
beta_2=0.999,
epsilon=1e-08,
decay=0.0) # Optimizer
model = DFNet.build(input_shape=INPUT_SHAPE, classes=NB_CLASSES)
model.compile(loss="categorical_crossentropy",
optimizer=OPTIMIZER,
metrics=["accuracy"])
return model
# we need a [Length x 1] x n shape as input to the DFNet (Tensorflow)
X_train = X_train[:, :, np.newaxis]
X_valid = X_valid[:, :, np.newaxis]
print(X_train.shape[0], 'training samples')
print(X_valid.shape[0], 'validation samples')
# convert class vectors to binary class matrices
y_train = np_utils.to_categorical(y_train, NB_CLASSES)
y_valid = np_utils.to_categorical(y_valid, NB_CLASSES)
print("Preparing Data for training")
# initialize the optimizer and model
print time.sleep(2)
model = DFNet.build(input_shape=INPUT_SHAPE, classes=NB_CLASSES)
model.compile(loss="categorical_crossentropy",
optimizer=OPTIMIZER,
metrics=["accuracy"])
print("Model compiled")
# Start training
history = model.fit(X_train,
y_train,
batch_size=BATCH_SIZE,
epochs=NB_EPOCH,
verbose=VERBOSE,
validation_data=(X_valid, y_valid))
# Save model
def create_model():
model = DFNet.build(input_shape=INPUT_SHAPE, classes=NB_CLASSES)
model.compile(loss="categorical_crossentropy",
optimizer=OPTIMIZER,
metrics=["accuracy"])
return model