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The first part of a SageMaker script for building an MXNet model that counts shapes in an image.
import logging
from pickle import load
import mxnet as mx
import numpy as np
from mxnet import autograd, nd, gluon
from mxnet.gluon import Trainer
from mxnet.gluon.loss import L2Loss
from mxnet.gluon.nn import Conv2D, MaxPool2D, Dropout, Flatten, Dense, Sequential
from mxnet.initializer import Xavier
def train(hyperparameters, channel_input_dirs, num_gpus):
batch_size = hyperparameters.get("batch_size", 64)
epochs = hyperparameters.get("epochs", 3)
training_dir = channel_input_dirs['training']"Loading data from {}".format(training_dir))
with open("{}/train/data.p".format(training_dir), "rb") as pickle:
train_nd = load(pickle)
with open("{}/validation/data.p".format(training_dir), "rb") as pickle:
validation_nd = load(pickle)
train_data = DataLoader(train_nd, batch_size, shuffle=True)
validation_data = DataLoader(validation_nd, batch_size, shuffle=True)
net = Sequential()
with net.name_scope():
net.add(Conv2D(channels=32, kernel_size=(3, 3),
padding=0, activation="relu"))
net.add(Conv2D(channels=32, kernel_size=(3, 3),
padding=0, activation="relu"))
net.add(MaxPool2D(pool_size=(2, 2)))
ctx = mx.gpu() if num_gpus > 0 else mx.cpu()
net.collect_params().initialize(Xavier(magnitude=2.24), ctx=ctx)
loss = L2Loss()
trainer = Trainer(net.collect_params(), optimizer="adam")
smoothing_constant = .01
for e in range(epochs):
moving_loss = 0
for i, (data, label) in enumerate(train_data):
data = data.as_in_context(ctx)
label = label.as_in_context(ctx)
with autograd.record():
output = net(data)
loss_result = loss(output, label)
curr_loss = nd.mean(loss_result).asscalar()
if (i == 0) and (e == 0):
moving_loss = curr_loss
moving_loss = (1 - smoothing_constant) * moving_loss + \
smoothing_constant * curr_loss
trn_total, trn_detected = calc_perf(net, ctx, train_data)
val_total, val_detected = calc_perf(net, ctx, validation_data)
log = "Epoch: {} loss: {:0.4f} perf_test: {:0.2f} perf_val: {:0.2f}" \
.format(e, moving_loss,
trn_detected / trn_total,
val_detected / val_total)
return net
def calc_perf(model, ctx, data_iter):
raw_predictions = np.array([])
rounded_predictions = np.array([])
actual_labels = np.array([])
for i, (data, label) in enumerate(data_iter):
data = data.as_in_context(ctx)
label = label.as_in_context(ctx)
output = model(data)
predictions = nd.round(output)
raw_predictions = np.append(raw_predictions,
rounded_predictions = np.append(rounded_predictions,
actual_labels = np.append(actual_labels,
results = np.concatenate((raw_predictions.reshape((-1, 1)),
rounded_predictions.reshape((-1, 1)),
actual_labels.reshape((-1, 1))), axis=1)
detected = 0
i = -1
for i in range(int(results.size / 3)):
if results[i][1] == results[i][2]:
detected += 1
return i + 1, detected
def save(net, model_dir):
y = net(mx.sym.var("data"))"{}/model.json".format(model_dir))
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