def resume_training_from_snapshot(weights_file,callbacks_list,initialEpoch=0):
model = create_model()
sgd = SGD(lr=0.01, decay=0.0004, momentum=0.9, nesterov=True)
# learning schedule callback
lrate = LearningRateScheduler(step_decay)
print "Loads weights from a snapshot"
# Loads parameters from a snapshot
model.load_weights(weights_file)
print "compliling the model"
model.compile(optimizer=sgd,loss=class_mode+'_crossentropy',metrics=['accuracy'])
model.summary()
train_generator,validation_generator = generateData()
# initial_epoch: Epoch at which to start training (useful for resuming a previous training run)
# teps_per_epoch: Total number of steps (batches of samples) to yield from generator
# before declaring one epoch finished and starting the next epoch.
# It should typically be equal to the number of unique samples of your dataset divided by the batch size.
history = model.fit_generator(train_generator,
steps_per_epoch=614,
validation_data=validation_generator,
validation_steps=20,
epochs=1000,callbacks=callbacks_list,
verbose=1,initial_epoch=initialEpoch)
saveModel(model)
# =================================================
# M A I N
# =================================================
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