mod2 = mx.mod.Module(softmax, context=contexts[1])
# --------------------------------------------------------------------------------
# Container module
# --------------------------------------------------------------------------------
mod_seq = mx.mod.SequentialModule()
mod_seq.add(mod1).add(mod2, take_labels=True, auto_wiring=True)
# --------------------------------------------------------------------------------
# Training
# --------------------------------------------------------------------------------
n_epoch = 2
batch_size = 100
basedir = os.path.dirname(__file__)
get_data.get_mnist(os.path.join(basedir, "data"))
train_dataiter = mx.io.MNISTIter(image=os.path.join(basedir, "data",
"train-images-idx3-ubyte"),
label=os.path.join(basedir, "data",
"train-labels-idx1-ubyte"),
data_shape=(784, ),
batch_size=batch_size,
shuffle=True,
flat=True,
silent=False,
seed=10)
val_dataiter = mx.io.MNISTIter(image=os.path.join(basedir, "data",
"t10k-images-idx3-ubyte"),
label=os.path.join(basedir, "data",
"t10k-labels-idx1-ubyte"),
import numpy as np
import logging
data = mx.symbol.Variable('data')
fc1 = mx.symbol.FullyConnected(data, name='fc1', num_hidden=128)
act1 = mx.symbol.Activation(fc1, name='relu1', act_type="relu")
fc2 = mx.symbol.FullyConnected(act1, name = 'fc2', num_hidden = 64)
act2 = mx.symbol.Activation(fc2, name='relu2', act_type="relu")
fc3 = mx.symbol.FullyConnected(act2, name='fc3', num_hidden=10)
softmax = mx.symbol.SoftmaxOutput(fc3, name = 'softmax')
n_epoch = 2
batch_size = 100
basedir = os.path.dirname(__file__)
get_data.get_mnist(os.path.join(basedir, "data"))
train_dataiter = mx.io.MNISTIter(
image=os.path.join(basedir, "data", "train-images-idx3-ubyte"),
label=os.path.join(basedir, "data", "train-labels-idx1-ubyte"),
data_shape=(784,),
batch_size=batch_size, shuffle=True, flat=True, silent=False, seed=10)
val_dataiter = mx.io.MNISTIter(
image=os.path.join(basedir, "data", "t10k-images-idx3-ubyte"),
label=os.path.join(basedir, "data", "t10k-labels-idx1-ubyte"),
data_shape=(784,),
batch_size=batch_size, shuffle=True, flat=True, silent=False)
################################################################################
# Intermediate-level API
################################################################################
