def _get_decoder(self):
output = nn.Dense(self._vocab_size, prefix='decoder0_')
return output
from mxnet import nd, init
from mxnet.gluon import nn, loss as gloss
from mxnet import autograd
net = nn.Sequential()
net.add(nn.Dense(256, activation='relu'))
net.add(nn.Dense(10))
net.initialize()
X = nd.random.uniform(shape=(2, 20))
Y = net(X)
# print(X)
print(net[0].params)
print(type(net[0].params))
print(net[0].weight)
print(type(net[0].weight))
# print(net[1].params, type(net[1].params))
# print(net[0].params['dense0_weight'] == net[0].weight)
# print(net[0].weight.data())
# print(net[0].weight.grad())
# print(net[1].bias.data())
# print(net.collect_params())
# print(net.collect_params('.*weight'))
# X.attach_grad()
# with autograd.record():
# y_hat = net(X)
# y_hat.backward()
# print(net[0].weight.grad())
def __init__(self, rnn_layer, vocab_size, **kwargs):
super(RNNModel, self).__init__(**kwargs)
self.rnn = rnn_layer
self.vocab_size = vocab_size
self.dense = nn.Dense(vocab_size)
def __init__(self,
query_units,
num_heads,
pos_embed_units: Optional[int] = None,
max_distance=None,
bidirectional=False,
num_buckets=None,
method='transformer_xl',
dropout: float = 0.0,
dtype='float32',
layout='NTK',
use_einsum=False):
"""
Parameters
----------
query_units
num_heads
pos_embed_units
max_distance
bidirectional
num_buckets
method
dropout
attention_dropout
query_add_bias
Add additional bias term to the query
scaled
dtype
layout
"""
super().__init__()
self._dropout = dropout
self._method = method
self._query_units = query_units
self._num_heads = num_heads
self._bidirectional = bidirectional
self._num_buckets = num_buckets
assert query_units % num_heads == 0, 'The units must be divisible by the number of heads.'
self._head_query_units = query_units // num_heads
self._max_distance = max_distance
self._pos_embed_units = pos_embed_units
self._dtype = dtype
self._use_einsum = use_einsum
self._layout = layout
if self._layout not in ['NKT', 'NTK', 'TNK']:
raise ValueError('layout="{}" is not supported'.format(
self._layout))
if method == 'transformer_xl':
if pos_embed_units is None:
pos_embed_units = self._num_heads * self._head_query_units
self._rel_pos_embed = SinusoidalPositionalEmbedding(
units=pos_embed_units, dtype=self._dtype)
self._rel_proj = nn.Dense(units=query_units,
in_units=pos_embed_units,
flatten=False,
use_bias=False,
dtype=self._dtype)
self._dropout_layer = nn.Dropout(dropout)
elif method == 'shaw':
assert self._max_distance is not None, 'Must set max_distance when method="shaw".'
if self._bidirectional:
vocab_size = self._max_distance * 2 + 1
else:
vocab_size = self._max_distance + 1
self._rel_pos_embed = LearnedPositionalEmbedding(
units=self._num_heads * self._head_query_units,
max_length=vocab_size,
weight_initializer=mx.init.Xavier(rnd_type="gaussian",
factor_type="in",
magnitude=1),
mode='wrap' if self._bidirectional else 'raise',
dtype=self._dtype)
elif method == 't5':
if self._num_buckets is None:
self._num_buckets = 32
if self._max_distance is None:
self._max_distance = 128
self._rel_pos_embed = BucketPositionalEmbedding(
units=num_heads,
num_buckets=self._num_buckets,
max_distance=self._max_distance,
bidirectional=self._bidirectional,
dtype=self._dtype)
else:
raise NotImplementedError(
'method="{}" is currently not supported!'.format(method))
N=X_tr.shape[0]
p=X_tr.shape[1]
X_train=nd.array(X_tr)
y_train=nd.array(y_tr)
batch_size=200
train_dataset = ArrayDataset(X_train, y_train)
train_data = DataLoader(train_dataset, batch_size=batch_size, shuffle=True)
X_test=nd.array(X_te).as_in_context(ctx)
y_test=nd.array(y_te).as_in_context(ctx)
net=nn.Sequential()
net.add(nn.Dense(1000,activation='relu'),nn.Dense(1, activation=None))
net.initialize(mx.init.Xavier(), ctx=ctx)
for i, (data, label) in enumerate(train_data):
aa=net(data.as_in_context(ctx))
break
net_initial=net
def init_masks_percents(net,p):
masks={}
percents={}
for i in enumerate(net):
masks[i[0]]=nd.ones(net[i[0]].weight.data().shape).as_in_context(ctx)
# -*- coding: utf-8 -*-
"""
Created on Tue Oct 20 09:46:48 2020
@author: DER
"""
import d2lzh as d2l
from mxnet import gluon, init
from mxnet.gluon import loss as gloss, nn
""" 3.10.1定义模型 """
net = nn.Sequential()
net.add(nn.Dense(256, activation="relu"), nn.Dense(10))
print(net)
net.initialize(init.Normal(sigma=0.01))
""" 3.10.2训练模型 """
batch_size = 256
train_iter, test_iter = d2l.load_data_fashion_mnist(batch_size)
loss = gloss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), "sgd", {"learning_rate": 0.5})
num_epochs = 5
d2l.train_ch3(net, train_iter, test_iter, loss, num_epochs, batch_size, None,
None, trainer)
def __init__(self, action_dim):
super(Actor, self).__init__()
self.action_dim = action_dim
self.dense0 = nn.Dense(400, activation='relu')
self.dense1 = nn.Dense(300, activation='relu')
self.dense2 = nn.Dense(self.action_dim)
def __init__(self, **kwargs): super(TailNegBlock, self).__init__(**kwargs) self.fc1 = nn.Dense(10, flatten=True) self.fc2 = nn.Dense(10, flatten=True)
def __init__(self, bert, prefix=None, params=None):
super(BertForQA, self).__init__(prefix=prefix, params=params)
self.bert = bert
with self.name_scope():
self.span_classifier = nn.Dense(units=2, flatten=False)
def __init__(self, **kwargs):
super(MLP, self).__init__(**kwargs)
self.hidden = nn.Dense(256, activation='relu')
self.output = nn.Dense(10)
def __init__(self, vocab_size=5, hidden_units=4):
super().__init__()
self.x2h_map = nn.Embedding(input_dim=vocab_size, output_dim=hidden_units)
self.h2h_map = nn.Dense(units=hidden_units, flatten=False)
self.vocab_map = nn.Dense(units=vocab_size, flatten=False)
loss.backward()
trainer.step(data.shape[0])
# Your first model should be a sequential network, with 3 layers. You first layer should have 16 hidden units, the second should have 8 hidden units and the last layer should the correct number of output units for the classification task at hand. You should add ReLU activations on all hidden layers, but not the output layer. You should define `network` in the cell below.
#
# **Hint**: You'll find classes in the `mxnet.gluon.nn` subpackage useful for this task.
# In[47]:
# YOUR CODE HERE
from mxnet.gluon import nn
network = nn.Sequential()
network.add(
nn.Dense(16, activation='relu'),
nn.Dense(8, activation='relu'),
nn.Dense(10)
)
#raise NotImplementedError()
# In[48]:
assert isinstance(network, mx.gluon.nn.Sequential)
assert len(network) == 3
assert isinstance(network[0], mx.gluon.nn.Dense)
assert network[0].act.name.endswith('relu')
assert network[0].weight.shape[0] == 16
assert isinstance(network[1], mx.gluon.nn.Dense)
def __init__(self,
channels,
init_block_channels,
final_block_channels,
residuals,
shortcuts,
kernel_sizes,
expansions,
bn_epsilon=1e-3,
bn_use_global_stats=False,
in_channels=3,
in_size=(224, 224),
classes=1000):
super(ProxylessNAS, self).__init__()
self.in_size = in_size
self.classes = classes
with self.name_scope():
self.features = nn.HybridSequential(prefix="")
self.features.add(
conv3x3_block(in_channels=in_channels,
out_channels=init_block_channels,
strides=2,
bn_epsilon=bn_epsilon,
bn_use_global_stats=bn_use_global_stats,
activation="relu6"))
in_channels = init_block_channels
for i, channels_per_stage in enumerate(channels):
stage = nn.HybridSequential(prefix="stage{}_".format(i + 1))
residuals_per_stage = residuals[i]
shortcuts_per_stage = shortcuts[i]
kernel_sizes_per_stage = kernel_sizes[i]
expansions_per_stage = expansions[i]
with stage.name_scope():
for j, out_channels in enumerate(channels_per_stage):
residual = (residuals_per_stage[j] == 1)
shortcut = (shortcuts_per_stage[j] == 1)
kernel_size = kernel_sizes_per_stage[j]
expansion = expansions_per_stage[j]
strides = 2 if (j == 0) and (i != 0) else 1
stage.add(
ProxylessUnit(
in_channels=in_channels,
out_channels=out_channels,
kernel_size=kernel_size,
strides=strides,
bn_epsilon=bn_epsilon,
bn_use_global_stats=bn_use_global_stats,
expansion=expansion,
residual=residual,
shortcut=shortcut))
in_channels = out_channels
self.features.add(stage)
self.features.add(
conv1x1_block(in_channels=in_channels,
out_channels=final_block_channels,
bn_epsilon=bn_epsilon,
bn_use_global_stats=bn_use_global_stats,
activation="relu6"))
in_channels = final_block_channels
self.features.add(nn.AvgPool2D(pool_size=7, strides=1))
self.output = nn.HybridSequential(prefix="")
self.output.add(nn.Flatten())
self.output.add(nn.Dense(units=classes, in_units=in_channels))
import matplotlib.pyplot as plt
import numpy as np
from mxnet import nd, gluon, init, autograd
import mxnet as mx
from mxnet import gluon, init, nd, autograd
from mxnet.gluon import data as gdata, nn, loss as gloss, utils as gutils
net = nn.Sequential()
net.add(nn.Dense(200, activation='tanh', use_bias=False),
nn.Dense(200, activation='tanh', use_bias=False),
nn.Dense(200, activation='tanh', use_bias=False),
nn.Dense(200, activation='tanh', use_bias=False))
X = nd.random.uniform(-1, 1, (1, 100))
net.initialize(force_reinit=True, init=init.Xavier())
X.attach_grad()
with autograd.record():
Y = net[:1](X)
Y.backward()
y0 = X.grad
y0 = y0.asnumpy()
y0 = np.round(y0, 1)
y0 = list(y0[0])
#print(y0)
y0 = sorted(y0)
print(y0)
y0set = sorted(set(y0))
print(y0set)
n = 0
#plt.figure()
x = []
# should be dot(X_, W)
E = self.attn(X_) # (n, hidden) -> (n, hidden)
attn_weights = F.softmax(E, axis=1) # (n, w)
attn_applied = F.elemwise_mul(attn_weights, X_) #(n,w)
output = self.c * (F.elemwise_mul(X_,
attn_weights)) + (1 - self.c) * X_
output = self.out(output) #(n,output_size)
return output
net = nn.Sequential()
with net.name_scope():
net.add(rnn.GRU(num_hidden, num_layers, layout='NTC')
) # T: sequence_length, N: batch_size, C: feature_dimension
net.add(nn.BatchNorm())
net.add(nn.Dense(sequence_length)) # this is to conver (nwc) to (nw)
net.add(Attn(sequence_length,
num_hidden)) # last layer attn, in (nw) o (nw)
net.collect_params().initialize(mx.init.Normal(sigma=0.02), ctx=ctx)
print(net.collect_params)
#params = net.collect_params()
#params.load('try3.params', ctx=ctx)
square_loss = gluon.loss.L2Loss()
learning_settings = {'learning_rate': 0.005, 'momentum': 0.9}
trainer = gluon.Trainer(net.collect_params(), 'sgd', learning_settings)
#metric = mx.metric.MSE()
epochs = 20
loss_sequence = []
for e in range(epochs):
def __init__(self,
levels,
channels,
classes=1000,
block=BasicBlock,
momentum=0.9,
norm_layer=BatchNorm,
norm_kwargs=None,
residual_root=False,
linear_root=False,
use_feature=False,
**kwargs):
super(DLA, self).__init__(**kwargs)
if norm_kwargs is None:
norm_kwargs = {}
norm_kwargs['momentum'] = momentum
self._use_feature = use_feature
self.channels = channels
self.base_layer = nn.HybridSequential('base')
self.base_layer.add(
nn.Conv2D(in_channels=3,
channels=channels[0],
kernel_size=7,
strides=1,
padding=3,
use_bias=False))
self.base_layer.add(norm_layer(in_channels=channels[0], **norm_kwargs))
self.base_layer.add(nn.Activation('relu'))
self.level0 = self._make_conv_level(channels[0], channels[0],
levels[0], norm_layer, norm_kwargs)
self.level1 = self._make_conv_level(channels[0],
channels[1],
levels[1],
norm_layer,
norm_kwargs,
stride=2)
self.level2 = Tree(levels[2],
block,
channels[1],
channels[2],
2,
level_root=False,
root_residual=residual_root,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs,
prefix='level2_')
self.level3 = Tree(levels[3],
block,
channels[2],
channels[3],
2,
level_root=True,
root_residual=residual_root,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs,
prefix='level3_')
self.level4 = Tree(levels[4],
block,
channels[3],
channels[4],
2,
level_root=True,
root_residual=residual_root,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs,
prefix='level4_')
self.level5 = Tree(levels[5],
block,
channels[4],
channels[5],
2,
level_root=True,
root_residual=residual_root,
norm_layer=norm_layer,
norm_kwargs=norm_kwargs,
prefix='level5_')
if not self._use_feature:
self.global_avg_pool = nn.GlobalAvgPool2D()
self.fc = nn.Dense(units=classes)
def __init__(self, **kwargs):
super(SimpleModel, self).__init__(**kwargs)
self.fc1 = nn.Dense(20)
self.fc2 = nn.Dense(10)
break
mnist_valid = gluon.data.vision.FashionMNIST(train=False)
valid_data = gluon.data.DataLoader(
mnist_valid.transform_first(transformer),
batch_size=batch_size, num_workers=4)
# Define model
net = nn.Sequential()
net.add(
nn.Conv2D(channels=6, kernel_size=5, activation='relu'),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Conv2D(channels=16, kernel_size=3, activation='relu'),
nn.MaxPool2D(pool_size=2, strides=2),
nn.Flatten(),
nn.Dense(120, activation='relu'),
nn.Dense(84, activation='relu'),
nn.Dense(10))
net.initialize(init=init.Xavier())
softmax_cross_entropy = gluon.loss.SoftmaxCrossEntropyLoss()
trainer = gluon.Trainer(net.collect_params(), 'sgd', {'learning_rate': 0.1})
def acc(output, label):
return (output.argmax(axis=1) == label.astype('float32')).mean().asscalar()
for epoch in range(10):
train_loss, train_acc, valid_acc = 0., 0., 0.
tic = time.time()
def __init__(self):
super(Critic, self).__init__()
self.dense0 = nn.Dense(400, activation='relu')
self.dense1 = nn.Dense(300, activation='relu')
self.dense2 = nn.Dense(1)
def __init__(self, in_units=0, **kwargs):
super(Net, self).__init__(**kwargs)
with self.name_scope():
self.dense0 = nn.Dense(5, in_units=in_units)
self.dense1 = nn.Dense(5, in_units=in_units)
def get_net():
net = nn.HybridSequential()
with net.name_scope():
net.add(net_resnet50.features)
net.add(nn.Dense(1062))
return net
def __init__(self, **kwargs):
super(Net, self).__init__(**kwargs)
with self.name_scope():
self.dense0 = nn.Dense(10, in_units=5, use_bias=False)
def __init__(self,
channels,
init_block_channels,
cardinality,
bottleneck_width,
group_widths,
refresh_steps,
bn_use_global_stats=False,
in_channels=3,
in_size=(224, 224),
classes=1000,
**kwargs):
super(CRUNet, self).__init__(**kwargs)
self.in_size = in_size
self.classes = classes
with self.name_scope():
self.features = nn.HybridSequential(prefix='')
self.features.add(
ResInitBlock(in_channels=in_channels,
out_channels=init_block_channels,
bn_use_global_stats=bn_use_global_stats))
in_channels = init_block_channels
for i, channels_per_stage in enumerate(channels):
stage = nn.HybridSequential(prefix="stage{}_".format(i + 1))
group_width = group_widths[i]
refresh_step = refresh_steps[i]
with stage.name_scope():
for j, out_channels in enumerate(channels_per_stage):
strides = 2 if (j == 0) and (i != 0) else 1
if group_width != 0:
if ((refresh_step == 0) and
(j == 0)) or ((refresh_step != 0) and
(j % refresh_step == 0)):
conv1_params = None
conv2_params = None
unit = CRUUnit(
in_channels=in_channels,
out_channels=out_channels,
strides=strides,
group_width=group_width,
bn_use_global_stats=bn_use_global_stats,
conv1_params=conv1_params,
conv2_params=conv2_params)
if conv1_params is None:
conv1_params = unit.body.conv1.conv.params
conv2_params = unit.body.conv2.params
stage.add(unit)
else:
stage.add(
ResUnit(
in_channels=in_channels,
out_channels=out_channels,
strides=strides,
cardinality=cardinality,
bottleneck_width=bottleneck_width,
bn_use_global_stats=bn_use_global_stats))
in_channels = out_channels
self.features.add(stage)
self.features.add(
PreResActivation(in_channels=in_channels,
bn_use_global_stats=bn_use_global_stats))
self.features.add(nn.AvgPool2D(pool_size=7, strides=1))
self.output = nn.HybridSequential(prefix='')
self.output.add(nn.Flatten())
self.output.add(nn.Dense(units=classes, in_units=in_channels))
def __init__(self, **kwargs):
super(Model1, self).__init__(**kwargs)
with self.name_scope():
self.layers = [nn.Dense(i * 10) for i in range(6)]
def __init__(self,
repeat=6,
penultimate_filters=4032,
stem_filters=96,
filters_multiplier=2,
classes=1000,
use_aux=True):
super(NASNetALarge, self).__init__()
filters = penultimate_filters // 24
self.conv0 = nn.HybridSequential(prefix='')
self.conv0.add(
nn.Conv2D(stem_filters, 3, padding=0, strides=2, use_bias=False))
self.conv0.add(nn.BatchNorm(momentum=0.1, epsilon=0.001))
self.cell_stem_0 = CellStem0(stem_filters,
num_filters=filters //
(filters_multiplier**2))
self.cell_stem_1 = CellStem1(num_filters=filters // filters_multiplier)
self.norm_1 = nn.HybridSequential(prefix='')
self.norm_1.add(
FirstCell(out_channels_left=filters // 2,
out_channels_right=filters))
for _ in range(repeat - 1):
self.norm_1.add(
NormalCell(out_channels_left=filters,
out_channels_right=filters))
self.reduction_cell_0 = ReductionCell0(out_channels_left=2 * filters,
out_channels_right=2 * filters)
self.norm_2 = nn.HybridSequential(prefix='')
self.norm_2.add(
FirstCell(out_channels_left=filters,
out_channels_right=2 * filters))
for _ in range(repeat - 1):
self.norm_2.add(
NormalCell(out_channels_left=2 * filters,
out_channels_right=2 * filters))
if use_aux:
self.out_aux = nn.HybridSequential(prefix='')
self.out_aux.add(
nn.Conv2D(filters // 3, kernel_size=1, use_bias=False))
self.out_aux.add(nn.BatchNorm(epsilon=0.001))
self.out_aux.add(nn.Activation('relu'))
self.out_aux.add(
nn.Conv2D(2 * filters, kernel_size=5, use_bias=False))
self.out_aux.add(nn.BatchNorm(epsilon=0.001))
self.out_aux.add(nn.Activation('relu'))
self.out_aux.add(nn.Dense(classes))
else:
self.out_aux = None
self.reduction_cell_1 = ReductionCell1(out_channels_left=4 * filters,
out_channels_right=4 * filters)
self.norm_3 = nn.HybridSequential(prefix='')
self.norm_3.add(
FirstCell(out_channels_left=2 * filters,
out_channels_right=4 * filters))
for _ in range(repeat - 1):
self.norm_3.add(
NormalCell(out_channels_left=4 * filters,
out_channels_right=4 * filters))
self.out = nn.HybridSequential(prefix='')
self.out.add(nn.Activation('relu'))
self.out.add(nn.GlobalAvgPool2D())
self.out.add(nn.Dropout(0.5))
self.out.add(nn.Dense(classes))
def __init__(self, **kwargs):
super(Model2, self).__init__(**kwargs)
with self.name_scope():
self.layers = dict()
self.layers['a'] = [nn.Dense(10), nn.Dense(10)]
def get_net():
net = nn.Sequential()
net.add(nn.Dense(64, activation="relu"), nn.Dense(32, activation="relu"),
nn.Dense(1))
return net
def __init__(self, **kwargs):
super(Model3, self).__init__(**kwargs)
with self.name_scope():
self.layers = nn.Sequential()
self.layers.add(*[nn.Dense(i * 10) for i in range(6)])
class MySequential(nn.Block):
def __init__(self, **kwargs):
super(MySequential, self).__init__(**kwargs)
def add(self, block):
self._children[block.name] = block
def forward(self, x):
for block in self._children.values():
x = block
return x
net = MySequential()
net.add(nn.Dense(256, activation='relu'))
net.initialize()
net(x)
class FancyMLP(nn.Block):
def __init__(self, **kwargs):
super(FancyMLP, self).__init__(**kwargs)
self.rand_weight = self.params.get_constant(
'rand_weight', nd.random.uniform(shape=(20, 20)))
self.dense = nn.Dense(20, activation='relu')
def forward(self, x):
x = self.dense(x)
print("Y_train: " + str(Y_train))
print("Y_test: " + str(Y_test))
## define network
num_classes = 2
num_hidden = 200
learning_rate = .01
epochs = 200
batch_size = 20
model = nn.Sequential()
with model.name_scope():
model.embed = nn.Embedding(voca_size, num_embed)
model.add(
rnn.LSTM(num_hidden, layout='NTC', dropout=0.7, bidirectional=False))
model.add(nn.Dense(num_classes))
def eval_accuracy(x, y, batch_size):
accuracy = mx.metric.Accuracy()
for i in range(x.shape[0] // batch_size):
data = x[i * batch_size:(i * batch_size + batch_size), ]
target = y[i * batch_size:(i * batch_size + batch_size), ]
output = model(data)
predictions = nd.argmax(output, axis=1)
accuracy.update(preds=predictions, labels=target)
return accuracy.get()[1]
