def __init__(self, shape=None, data_type=None, needs_gradient=False, is_sparse=False,
dynamic_axes = [Axis.default_dynamic_axis(), Axis.default_batch_axis()], name=''):
shape = utils.sanitize_shape(shape)
if data_type is None:
data_type = FLOAT_32
dtype = utils.sanitize_dtype_cntk(data_type)
super(Variable, self).__init__(shape, is_sparse, dtype, needs_gradient, name, dynamic_axes)
break
trainer.train_minibatch(data)
loss_numer += trainer.previous_minibatch_loss_average() * trainer.previous_minibatch_sample_count() # too much code for something this simple
loss_denom += trainer.previous_minibatch_sample_count()
metric_numer += trainer.previous_minibatch_evaluation_average() * trainer.previous_minibatch_sample_count()
metric_denom += trainer.previous_minibatch_sample_count()
print_training_progress(trainer, mbs if mbs > 10 else 0, num_mbs_to_show_result)
t += num_samples[slot_labels]
#print (num_samples[slot_labels], t)
mbs += 1
print("--- EPOCH {} DONE: loss = {:0.6f} * {}, metric = {:0.1f}% * {} ---".format(epoch+1, loss_numer/loss_denom, loss_denom, metric_numer/metric_denom*100.0, metric_denom))
return loss_numer/loss_denom, metric_numer/metric_denom
#############################
# main function boilerplate #
#############################
if __name__=='__main__':
# TODO: get closure on Amit's feedback "Not the right pattern as we discussed over email. Please change to set_default_device(gpu(0))"
#set_gpu(0)
#set_computation_network_trace_level(1) # TODO: remove debugging facilities once this all works
reader = create_reader(data_dir + "/atis.train.ctf")
model = create_model(_inf=_Infer(shape=input_dim, axis=[Axis.default_batch_axis(), Axis.default_dynamic_axis()]))
# TODO: Currently this fails with a mismatch error if axes ^^ are given in opposite order. I think it shouldn't.
# train
train(reader, model, max_epochs=8)
# test (TODO)
reader = create_reader(data_dir + "/atis.test.ctf")
#test(reader, model_dir + "/slu.cmf") # TODO: what is the correct pattern here?
def main():
#set_computation_network_trace_level(1) # TODO: remove debugging facilities once this all works
reader = create_reader(data_dir + "/atis.train.ctf")
model = create_model(_inf=_Infer(shape=input_dim, axis=[Axis.default_batch_axis(), Axis.default_dynamic_axis()]))
# TODO: Currently this fails with a mismatch error if axes ^^ are given in opposite order. I think it shouldn't.
# train
train(reader, model, max_epochs=8)
# test (TODO)
reader = create_reader(data_dir + "/atis.test.ctf")
