Mxnet速查_CPU和GPU的mnist预测训练_模型导出_模型导入再预测_导出onnx并预测
需要做点什么
方便广大烟酒生研究生、人工智障炼丹师算法工程师快速使用mxnet，所以特写此文章，默认使用者已有基本的深度学习概念、数据集概念。
系统环境
python 3.7.4
mxnet 1.9.0
mxnet-cu112 1.9.0
onnx 1.9.0
onnxruntime-gpu 1.9.0
数据准备
MNIST数据集csv文件是一个42000x785的矩阵
42000表示有42000张图片
785中第一列是图片的类别(0,1,2,..,9),第二列到最后一列是图片数据向量 (28x28的图片张成784的向量)， 数据集长这个样子:
1 0 0 0 0 0 0 0 0 0 ..
0 0 0 0 0 0 0 0 0 0 ..
1 0 0 0 0 0 0 0 0 0 ..
1. 导入需要的包
import time
import copy
import onnx
import logging
import platform
import mxnet as mx
import numpy as np
import pandas as pd
import onnxruntime as ort
from sklearn.metrics import accuracy_score
logger = logging.getLogger()
logger.setLevel(logging.DEBUG)
# Mxnet Chcek
if platform.system().lower() != 'windows':
print(mx.runtime.feature_list())
print(mx.context.num_gpus())
a = mx.nd.ones((2, 3), mx.cpu())
b = a * 2 + 1
print(b)
运行输出
[ CUDA, CUDNN, NCCL, CUDA_RTC, TENSORRT, CPU_SSE, CPU_SSE2, CPU_SSE3, CPU_SSE4_1, CPU_SSE4_2, CPU_SSE4A, CPU_AVX, CPU_AVX2, OPENMP, SSE, F16C, JEMALLOC, BLAS_OPEN, BLAS_ATLAS, BLAS_MKL, BLAS_APPLE, LAPACK, MKLDNN, OPENCV, CAFFE, PROFILER, DIST_KVSTORE, CXX14, INT64_TENSOR_SIZE, SIGNAL_HANDLER, DEBUG, TVM_OP]
1
[[3. 3. 3.]
[3. 3. 3.]]
<NDArray 2x3 @cpu(0)>
2. 参数准备
N_EPOCH = 1
N_BATCH = 32
N_BATCH_NUM = 900
S_DATA_PATH = r"mnist_train.csv"
S_MODEL_PATH = r"mxnet_cnn"
S_SYM_PATH = './mxnet_cnn-symbol.json'
S_PARAMS_PATH = './mxnet_cnn-0001.params'
S_ONNX_MODEL_PATH = './mxnet_cnn.onnx'
S_DEVICE, N_DEVICE_ID, S_DEVICE_FULL = "cuda", 0, "cuda:0"
# S_DEVICE, N_DEVICE_ID, S_DEVICE_FULL = "cpu", 0, "cpu"
CTX = mx.cpu() if S_DEVICE == "cpu" else mx.gpu(N_DEVICE_ID)
B_IS_UNIX = True
3. 读取数据
df = pd.read_csv(S_DATA_PATH, header=None)
print(df.shape)
np_mat = np.array(df)
print(np_mat.shape)
X = np_mat[:, 1:]
Y = np_mat[:, 0]
X = X.astype(np.float32) / 255
X_train = X[:N_BATCH * N_BATCH_NUM]
X_test = X[N_BATCH * N_BATCH_NUM:]
Y_train = Y[:N_BATCH * N_BATCH_NUM]
Y_test = Y[N_BATCH * N_BATCH_NUM:]
X_train = X_train.reshape(X_train.shape[0], 1, 28, 28)
X_test = X_test.reshape(X_test.shape[0], 1, 28, 28)
print(X_train.shape)
print(Y_train.shape)
print(X_test.shape)
print(Y_test.shape)
train_iter = mx.io.NDArrayIter(X_train, Y_train, batch_size=N_BATCH)
test_iter = mx.io.NDArrayIter(X_test, Y_test, batch_size=N_BATCH)
test_iter_2 = copy.copy(test_iter)
运行输出
(37800, 785)
(37800, 785)
(28800, 1, 28, 28)
(28800,)
(9000, 1, 28, 28)
(9000,)
4. 模型构建
net = mx.gluon.nn.HybridSequential()
with net.name_scope():
net.add(mx.gluon.nn.Conv2D(channels=32, kernel_size=3, activation='relu')) # bx28x28 ==>
net.add(mx.gluon.nn.MaxPool2D(pool_size=2, strides=2))
net.add(mx.gluon.nn.Flatten())
net.add(mx.gluon.nn.Dense(128, activation="relu"))
net.add(mx.gluon.nn.Dense(10))
net.hybridize()
print(net)
net.collect_params().initialize(mx.init.Xavier(), ctx=CTX)
softmax_cross_entropy = mx.gluon.loss.SoftmaxCrossEntropyLoss()
trainer = mx.gluon.Trainer(net.collect_params(), 'adam', {'learning_rate': .001})
运行输出
HybridSequential(
(0): Conv2D(None -> 32, kernel_size=(3, 3), stride=(1, 1), Activation(relu))
(1): MaxPool2D(size=(2, 2), stride=(2, 2), padding=(0, 0), ceil_mode=False, global_pool=False, pool_type=max, layout=NCHW)
(2): Flatten
(3): Dense(None -> 128, Activation(relu))
(4): Dense(None -> 10, linear)
)
5. 模型训练
for epoch in range(N_EPOCH):
for batch_num, itr in enumerate(train_iter):
data = itr.data[0].as_in_context(CTX)
label = itr.label[0].as_in_context(CTX)
with mx.autograd.record():
output = net(data) # Run the forward pass
loss = softmax_cross_entropy(output, label) # Compute the loss
loss.backward()
trainer.step(data.shape[0])
if batch_num % 50 == 0: # Print loss once in a while
curr_loss = mx.nd.mean(loss) # .asscalar()
pred = mx.nd.argmax(output, axis=1)
np_pred, np_lable = pred.asnumpy(), label.asnumpy()
f_acc = accuracy_score(np_lable, np_pred)
print(f"Epoch: {epoch}; Batch {batch_num}; ACC {f_acc}")
print(f"loss: {curr_loss}")
print()
# print("Epoch: %d; Batch %d; Loss %s; ACC %f" %
# (epoch, batch_num, str(curr_loss), f_acc))
print()
运行输出
Epoch: 0; Batch 0; ACC 0.09375
loss:
[2.2868602]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 50; ACC 0.875
loss:
[0.512461]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 100; ACC 0.90625
loss:
[0.43415746]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 150; ACC 0.84375
loss:
[0.3854709]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 200; ACC 1.0
loss:
[0.04192135]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 250; ACC 0.90625
loss:
[0.21156572]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 300; ACC 0.9375
loss:
[0.15938525]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 350; ACC 1.0
loss:
[0.0379494]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 400; ACC 0.96875
loss:
[0.17104594]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 450; ACC 0.96875
loss:
[0.12192786]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 500; ACC 0.96875
loss:
[0.09210478]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 550; ACC 0.9375
loss:
[0.13728428]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 600; ACC 0.96875
loss:
[0.0762211]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 650; ACC 0.96875
loss:
[0.12162409]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 700; ACC 1.0
loss:
[0.04334489]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 750; ACC 1.0
loss:
[0.06458903]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 800; ACC 0.96875
loss:
[0.07410634]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 850; ACC 0.96875
loss:
[0.14233188]
<NDArray 1 @gpu(0)>
6.模型预测
for batch_num, itr in enumerate(test_iter_2):
data = itr.data[0].as_in_context(CTX)
label = itr.label[0].as_in_context(CTX)
output = net(data) # Run the forward pass
loss = softmax_cross_entropy(output, label) # Compute the loss
if batch_num % 50 == 0: # Print loss once in a while
curr_loss = mx.nd.mean(loss) # .asscalar()
pred = mx.nd.argmax(output, axis=1)
np_pred, np_lable = pred.asnumpy(), label.asnumpy()
f_acc = accuracy_score(np_lable, np_pred)
print(f"Epoch: {epoch}; Batch {batch_num}; ACC {f_acc}")
print(f"loss: {curr_loss}")
print()
运行输出
Epoch: 0; Batch 0; ACC 0.96875
loss:
[0.22968824]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 50; ACC 0.96875
loss:
[0.05668993]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 100; ACC 0.96875
loss:
[0.08171713]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 150; ACC 1.0
loss:
[0.02264522]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 200; ACC 0.96875
loss:
[0.080383]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 250; ACC 1.0
loss:
[0.03774196]
<NDArray 1 @gpu(0)>
7.模型保存
net.export(S_MODEL_PATH, epoch=N_EPOCH) # 保存模型结构和全部参数
8.模型加载和加载模型使用
print("load net and do test")
load_net = mx.gluon.nn.SymbolBlock.imports(S_SYM_PATH, ['data'], S_PARAMS_PATH, ctx=CTX) # 加载模型
print("load ok")
for batch_num, itr in enumerate(test_iter): # Test
data = itr.data[0].as_in_context(CTX)
label = itr.label[0].as_in_context(CTX)
output = load_net(data) # Run the forward pass
loss = softmax_cross_entropy(output, label) # Compute the loss
if batch_num % 50 == 0: # Print loss once in a while
curr_loss = mx.nd.mean(loss) # .asscalar()
pred = mx.nd.argmax(output, axis=1)
np_pred, np_lable = pred.asnumpy(), label.asnumpy()
f_acc = accuracy_score(np_lable, np_pred)
print(f"Epoch: {epoch}; Batch {batch_num}; ACC {f_acc}")
print(f"loss: {curr_loss}")
print()
print("finish")
运行输出
load net and do test
load ok
Epoch: 0; Batch 0; ACC 0.96875
loss:
[0.22968824]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 50; ACC 0.96875
loss:
[0.05668993]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 100; ACC 0.96875
loss:
[0.08171713]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 150; ACC 1.0
loss:
[0.02264522]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 200; ACC 0.96875
loss:
[0.080383]
<NDArray 1 @gpu(0)>
Epoch: 0; Batch 250; ACC 1.0
loss:
[0.03774196]
<NDArray 1 @gpu(0)>
finish
9.导出ONNX
if platform.system().lower() != 'windows':
mx.onnx.export_model(S_SYM_PATH, S_PARAMS_PATH, [(32, 1, 28, 28)], [np.float32], S_ONNX_MODEL_PATH, verbose=True, dynamic=True)
运行输出
INFO:root:Converting json and weight file to sym and params
INFO:root:Converting idx: 0, op: null, name: data
INFO:root:Converting idx: 1, op: null, name: hybridsequential0_conv0_weight
INFO:root:Converting idx: 2, op: null, name: hybridsequential0_conv0_bias
INFO:root:Converting idx: 3, op: Convolution, name: hybridsequential0_conv0_fwd
INFO:root:Converting idx: 4, op: Activation, name: hybridsequential0_conv0_relu_fwd
INFO:root:Converting idx: 5, op: Pooling, name: hybridsequential0_pool0_fwd
INFO:root:Converting idx: 6, op: Flatten, name: hybridsequential0_flatten0_flatten0
INFO:root:Converting idx: 7, op: null, name: hybridsequential0_dense0_weight
INFO:root:Converting idx: 8, op: null, name: hybridsequential0_dense0_bias
INFO:root:Converting idx: 9, op: FullyConnected, name: hybridsequential0_dense0_fwd
INFO:root:Converting idx: 10, op: Activation, name: hybridsequential0_dense0_relu_fwd
INFO:root:Converting idx: 11, op: null, name: hybridsequential0_dense1_weight
INFO:root:Converting idx: 12, op: null, name: hybridsequential0_dense1_bias
INFO:root:Converting idx: 13, op: FullyConnected, name: hybridsequential0_dense1_fwd
INFO:root:Output node is: hybridsequential0_dense1_fwd
INFO:root:Input shape of the model [(32, 1, 28, 28)]
INFO:root:Exported ONNX file ./mxnet_cnn.onnx saved to disk
10. 加载ONNX并运行
if platform.system().lower() != 'windows':
model = onnx.load(S_ONNX_MODEL_PATH)
print(onnx.checker.check_model(model)) # Check that the model is well formed
# print(onnx.helper.printable_graph(model.graph)) # Print a human readable representation of the graph
ls_input_name, ls_output_name = [input.name for input in model.graph.input], [output.name for output in model.graph.output]
print("input name ", ls_input_name)
print("output name ", ls_output_name)
s_input_name = ls_input_name[0]
x_input = X_train[:N_BATCH*2, :, :, :].astype(np.float32)
ort_val = ort.OrtValue.ortvalue_from_numpy(x_input, S_DEVICE, N_DEVICE_ID)
print("val device ", ort_val.device_name())
print("val shape ", ort_val.shape())
print("val data type ", ort_val.data_type())
print("is_tensor ", ort_val.is_tensor())
print("array_equal ", np.array_equal(ort_val.numpy(), x_input))
providers = 'CUDAExecutionProvider' if S_DEVICE == "cuda" else 'CPUExecutionProvider'
print("providers ", providers)
ort_session = ort.InferenceSession(S_ONNX_MODEL_PATH, providers=[providers]) # gpu运行
ort_session.set_providers([providers])
outputs = ort_session.run(None, {s_input_name: ort_val})
print("sess env ", ort_session.get_providers())
print(type(outputs))
print(outputs[0])
'''
For example ['CUDAExecutionProvider', 'CPUExecutionProvider']
means execute a node using CUDAExecutionProvider if capable, otherwise execute using CPUExecutionProvider.
'''
运行输出
None
input name ['data', 'hybridsequential0_conv0_weight', 'hybridsequential0_conv0_bias', 'hybridsequential0_dense0_weight', 'hybridsequential0_dense0_bias', 'hybridsequential0_dense1_weight', 'hybridsequential0_dense1_bias']
output name ['hybridsequential0_dense1_fwd']
val device cuda
val shape [64, 1, 28, 28]
val data type tensor(float)
is_tensor True
array_equal True
providers CUDAExecutionProvider
sess env ['CUDAExecutionProvider', 'CPUExecutionProvider']
<class 'list'>
[[-2.69336128e+00 8.42524242e+00 -3.34120363e-01 -1.17912292e+00
3.82278800e-01 -3.60794234e+00 3.58125120e-01 -2.58064723e+00
1.55215383e+00 -2.03553891e+00]
[ 1.02665892e+01 -6.65782404e+00 -2.04501271e-01 -2.25653172e+00
-6.31941366e+00 1.13084137e+00 -3.83885235e-01 8.22283030e-01
-1.21192622e+00 3.33601260e+00]
[-3.27186418e+00 1.00050325e+01 5.39114550e-02 -1.44938648e+00
-9.89762247e-01 -2.09957671e+00 -1.49389958e+00 6.52510405e-01
1.73153889e+00 -1.25597775e+00]
[ 5.72116375e-01 -3.36192799e+00 -6.68362260e-01 -2.81247520e+00
8.36382389e+00 -3.67477946e-02 2.23792076e+00 -2.91093756e-02
-4.56922323e-01 -6.77382052e-01]
[ 1.18602552e+01 -5.09683752e+00 4.54203248e-01 -2.55723000e+00
-8.68753910e+00 6.96948707e-01 -1.50591761e-01 -3.62227589e-01
9.83437955e-01 7.46711075e-01]
[ 7.33289337e+00 -6.65414715e+00 1.57180679e+00 -2.62657452e+00
4.11511570e-01 -1.35336161e+00 -1.40558392e-01 3.81030589e-01
1.73799121e+00 8.02671254e-01]
[-3.02898431e+00 1.26861107e+00 -2.04946566e+00 -2.52499342e-01
-2.73597687e-01 -3.01714039e+00 -7.10914516e+00 1.10452967e+01
-5.82177579e-01 1.86712158e+00]
[-7.78098392e+00 -6.01984358e+00 1.23355007e+00 1.18682652e+01
-9.83472538e+00 8.27242088e+00 -1.02135544e+01 3.95661980e-01
6.63226461e+00 3.33681512e+00]
[-2.72245955e+00 -6.74849796e+00 -6.24665642e+00 3.11165476e+00
-4.71174330e-01 1.22390661e+01 -1.23519528e+00 -1.24356663e+00
1.26693976e+00 5.81862879e+00]
[-5.65229607e+00 -1.25138938e+00 3.68380380e+00 1.24947300e+01
-8.21508980e+00 1.61641145e+00 -8.01925087e+00 8.37018967e-01
-2.64613247e+00 7.92313635e-01]
[-3.73405719e+00 -3.41621947e+00 -7.94842839e-01 4.55352879e+00
-2.28238964e+00 1.88887548e+00 -5.84129477e+00 6.03430390e-01
1.05920439e+01 2.25430655e+00]
[-5.44103146e+00 -5.48421431e+00 -3.62234282e+00 1.20194650e+00
3.48899674e+00 1.50794566e+00 -6.30612850e+00 4.01568127e+00
1.61318648e+00 9.87832165e+00]
[-3.34073186e+00 8.10987663e+00 -6.43497527e-01 -1.64372277e+00
-4.42907363e-01 -1.46176386e+00 -8.56327295e-01 5.20323329e-02
1.73289025e+00 -8.17061365e-01]
[-6.88457203e+00 1.38391244e+00 1.33096969e+00 1.28132534e+01
-6.20939922e+00 1.48244214e+00 -6.59804583e+00 -1.38118923e+00
4.26289368e+00 -1.22962976e+00]
[-6.09051991e+00 -3.15275192e+00 1.79273260e+00 9.92699528e+00
-5.97349882e+00 3.68225765e+00 -6.47421646e+00 -1.99264419e+00
2.15714622e+00 2.32836318e+00]
[-3.25946307e+00 8.14360428e+00 -1.00535810e+00 -2.37552500e+00
2.38139248e+00 -2.92597318e+00 -1.54173911e+00 2.25682306e+00
-2.83430189e-01 -1.33554244e+00]
[-2.99147058e+00 3.86941671e+00 8.82810593e+00 2.20121431e+00
-8.40485859e+00 -8.66728902e-01 -5.97998762e+00 -5.21699572e+00
5.80638123e+00 -2.57314467e+00]
[ 8.64277363e+00 -4.99241495e+00 2.84688592e+00 -4.15350378e-01
-1.87728360e-01 -2.40291572e+00 4.42544132e-01 -4.54446167e-01
-1.88113344e+00 -1.23334014e+00]
[-2.00169897e+00 -2.65497804e+00 1.18750989e+00 9.70900059e-01
-4.53840446e+00 -2.65584946e+00 -8.23472023e+00 9.93836498e+00
-5.57100773e-01 3.42955470e+00]
[-3.57249069e+00 -5.03176594e+00 -1.79369414e+00 -5.03321826e-01
-1.97100627e+00 9.01608944e+00 6.62497377e+00 -5.48222637e+00
6.09256268e+00 -4.71334040e-01]
[-5.27715540e+00 -7.84428477e-01 -6.26944721e-01 3.87298250e+00
-1.88836837e+00 1.15252662e+00 -2.98473048e+00 -3.10233998e+00
9.71112537e+00 3.10839200e+00]
[-9.50223565e-01 -6.47654009e+00 2.26750326e+00 1.95419586e+00
1.68217969e+00 1.66003108e+00 9.82697105e+00 -9.94868219e-01
-2.03924966e+00 -1.88321277e-01]
[-3.11575246e+00 3.43664408e+00 1.19877796e+01 4.36916590e+00
-1.17812777e+01 -1.69431508e+00 -5.82668829e+00 -5.09948444e+00
4.15738583e+00 -4.30461359e+00]
[ 9.72177792e+00 -5.31352401e-01 -1.21784186e+00 -1.07392669e+00
-7.11223555e+00 1.67838800e+00 1.01826215e+00 -8.88240516e-01
6.95959151e-01 2.38748863e-01]
[-2.06619406e+00 1.86608231e+00 1.12100420e+01 4.22539425e+00
-1.21493711e+01 -4.57662535e+00 -6.88935089e+00 -9.81215835e-01
3.87611055e+00 -3.28470826e+00]
[-6.73031902e+00 -2.54390073e+00 -1.10151446e+00 1.51524162e+01
-1.10052080e+01 6.60323954e+00 -7.94388771e+00 3.31939721e+00
-1.40840662e+00 2.65730071e+00]
[-1.96954179e+00 -1.13817227e+00 9.40351069e-01 -1.75684047e+00
3.60373807e+00 2.01377797e+00 1.00558109e+01 -1.10547984e+00
5.17374456e-01 -3.94047165e+00]
[-5.81787634e+00 -1.20211565e+00 -3.53216052e+00 1.17569458e+00
4.21314144e+00 -2.53644252e+00 -7.64466667e+00 4.19782829e+00
4.28840429e-01 1.04579344e+01]
[-4.20310974e+00 -3.19272375e+00 -4.62792778e+00 2.71683741e+00
4.43899345e+00 3.31357956e-01 -6.24839544e+00 3.80388188e+00
-1.22620119e-02 9.65024757e+00]
[-8.26945066e-01 -5.25947523e+00 3.72772887e-02 2.30585241e+00
-4.95726252e+00 -1.19987357e+00 -1.20395079e+01 1.53253164e+01
-2.10372299e-01 1.89387524e+00]
[-5.09596729e+00 -7.76027665e-02 -9.53466833e-01 2.89041376e+00
-1.50858855e+00 2.27854323e+00 -1.95591903e+00 -3.15785193e+00
1.00103540e+01 1.08987451e+00]
[-4.01680946e-01 -4.62062168e+00 3.90530303e-02 -1.66790807e+00
5.43311167e+00 -1.78802896e+00 -2.88405848e+00 2.93439984e+00
-2.16558409e+00 8.71198368e+00]
[-1.29969406e+00 -3.92871022e+00 -3.82151055e+00 -2.93831253e+00
1.03674269e+01 8.88044477e-01 7.88922787e-01 3.86107159e+00
1.60807288e+00 -3.76913965e-01]
[-2.25020099e+00 -8.17249107e+00 -1.82360613e+00 -5.90175152e-01
4.72407389e+00 9.39436078e-01 -3.85674310e+00 3.95303702e+00
1.83473241e+00 9.13874054e+00]
[-3.26617742e+00 -2.91517663e+00 8.37770653e+00 1.61820054e-01
-2.98638320e+00 -2.47211266e+00 5.08574843e-01 4.65608168e+00
2.66001201e+00 -4.67262363e+00]
[-2.29874635e+00 7.77097034e+00 1.11359918e+00 -2.06103897e+00
-7.61267126e-01 1.00877440e+00 1.47708499e+00 -1.20483887e+00
1.99922264e+00 -3.81118345e+00]
[-6.87821198e+00 -9.18823600e-01 2.16773844e+00 1.07671242e+01
-7.48823595e+00 2.90310860e+00 -1.02075748e+01 -3.83400464e+00
4.76818371e+00 4.06564474e+00]
[-2.06487226e+00 8.76828384e+00 1.10449910e+00 -2.29669046e+00
-1.15668392e+00 -2.50351834e+00 -1.69508122e-02 -1.05916834e+00
1.91057479e+00 -2.64592767e+00]
[-2.24318981e+00 9.02024174e+00 1.38990092e+00 -2.72154903e+00
1.46101296e-01 -4.43454313e+00 -8.21092844e-01 -2.40808502e-01
3.36577922e-01 -2.63193059e+00]
[-4.35961342e+00 -7.74704576e-01 -2.74345660e+00 -3.27951574e+00
1.50971518e+01 -2.80669570e+00 -1.28740633e+00 3.94157290e+00
4.20372874e-01 8.37333024e-01]
[-2.80749345e+00 -3.33036280e+00 -1.00865018e+00 4.57633829e+00
-5.03952360e+00 2.93345642e+00 -8.54609489e+00 2.26549125e+00
4.73208952e+00 5.93849993e+00]
[-3.31042576e+00 9.97719002e+00 4.38573778e-01 -1.35296178e+00
-1.21057940e+00 -2.46178842e+00 -8.34564090e-01 2.19030753e-01
2.03147411e+00 -1.80211437e+00]
[-2.14534068e+00 -2.93023801e+00 4.41405416e-01 -2.29865336e+00
1.47422533e+01 -1.86358702e+00 4.61042017e-01 -6.20108247e-01
-1.36792552e+00 2.14018896e-01]
[-2.64241481e+00 -2.28332114e+00 2.01109338e+00 9.67352509e-01
6.09287119e+00 -2.35626236e-01 -3.02941656e+00 4.32772923e+00
-4.63955021e+00 3.73136783e+00]
[-4.55847168e+00 1.04014337e+00 9.12987328e+00 2.06433630e+00
-1.67355919e+00 -1.49593079e+00 4.09124941e-01 2.41894865e+00
-6.86871633e-02 -4.42179346e+00]
[-6.10608578e-01 -2.73860097e+00 -1.09864855e+00 -5.68899512e-01
2.45831108e+00 5.50326490e+00 1.22601585e+01 -5.23877192e+00
2.11066246e+00 -2.98584485e+00]
[-5.96745872e+00 -1.91458237e+00 -3.10774088e-01 1.00216856e+01
-3.81997776e+00 7.14399862e+00 -4.61386251e+00 -5.18248987e+00
4.25162363e+00 1.18878789e-01]
[-4.24126434e+00 -9.63249326e-01 1.06391013e+00 4.45316315e+00
-4.47125340e+00 -1.21906054e+00 -9.75789547e+00 1.10335569e+01
-1.17632782e+00 8.78942788e-01]
[ 3.76867175e-01 -4.75102758e+00 -2.59345794e+00 -3.96257102e-01
-7.50329159e-03 1.81642962e+00 -6.01041269e+00 9.97849655e+00
-2.57468176e+00 5.00644207e+00]
[-2.21995306e+00 -4.23465443e+00 -2.19536662e+00 -3.71420813e+00
1.49460211e+01 -2.73240638e+00 3.03538777e-02 4.29108334e+00
1.21963143e+00 6.85284913e-01]
[-1.93794692e+00 -3.39166284e+00 -3.41372967e-01 -2.16144085e-01
1.32588074e-01 -3.83050554e-02 -7.32452822e+00 9.68561840e+00
-1.16044319e+00 3.63913298e+00]
[-3.31972694e+00 -4.84879112e+00 -3.41381001e+00 1.93332338e+00
-5.16150045e+00 1.05730495e+01 1.52961123e+00 -4.64916992e+00
4.11477995e+00 4.80105543e+00]
[-3.22445488e+00 1.01509037e+01 6.03635088e-02 -1.48001885e+00
-3.28380197e-01 -2.36782789e+00 -8.66441727e-01 6.68077886e-01
1.45576179e+00 -1.95271623e+00]
[-5.64618587e+00 -3.71156931e+00 -2.31174397e+00 1.76912701e+00
2.95111752e+00 2.09562635e+00 -5.34609461e+00 -2.59399921e-01
1.10373080e+00 1.10444403e+01]
[ 1.32743053e+01 -5.11389780e+00 1.10446036e+00 -5.01595545e+00
-4.87907743e+00 4.46035750e-02 4.57046556e+00 -1.76434004e+00
-5.40824793e-02 -1.01547205e+00]
[-4.27589798e+00 1.26832044e+00 6.49948978e+00 -8.06193352e-02
1.34645328e-01 6.92090929e-01 -8.92272711e-01 2.15252757e+00
-9.95365143e-01 -2.46636438e+00]
[-5.08349514e+00 -1.79646879e-01 9.57399654e+00 3.35643005e+00
-3.42183971e+00 -2.33653522e+00 -1.98645079e+00 1.50538552e+00
-1.43313253e+00 -9.75638926e-01]
[-8.48450565e+00 -4.32870531e+00 -1.54253757e+00 1.61029205e+01
-8.41084957e+00 5.45092726e+00 -1.00705996e+01 2.31331086e+00
1.05400957e-01 5.19563723e+00]
[-4.89484310e+00 -4.33120441e+00 -3.71932483e+00 -1.18670315e-01
4.70187807e+00 2.67010808e-01 -5.52650118e+00 5.62736416e+00
5.05499423e-01 8.69004250e+00]
[-1.94048929e+00 7.98250961e+00 -3.75457823e-01 -2.09433126e+00
-4.63896915e-02 -2.64650702e+00 -5.73348761e-01 -1.34597674e-01
1.26143038e+00 -1.32178509e+00]
[-6.36190224e+00 5.44552183e+00 1.89667892e+00 3.91665459e+00
-1.99860716e+00 2.76620483e+00 3.57649279e+00 -3.53898597e+00
-1.21411644e-01 -2.60307193e+00]
[-3.60333633e+00 4.92228556e+00 2.87770915e+00 1.31902504e+00
-4.28756446e-01 -3.29862523e+00 -2.29294825e+00 -1.10349190e+00
3.81862259e+00 -1.23572731e+00]
[ 1.06328213e+00 -6.86575174e+00 -3.61938500e+00 1.15000987e+00
-2.32747698e+00 1.32029047e+01 -3.19671059e+00 8.91836137e-02
4.70500660e+00 2.51928687e+00]
[ 1.00363417e+01 -8.02793884e+00 9.38569680e-02 -2.78522468e+00
-2.84671545e+00 2.95867848e+00 4.23545599e+00 2.52083421e+00
-3.35666537e+00 1.45630157e+00]]
你甚至不愿意Start的Github
Mxnet速查_CPU和GPU的mnist预测训练_模型导出_模型导入再预测_导出onnx并预测的更多相关文章
- Keras速查_CPU和GPU的mnist预测训练_模型导出_模型导入再预测_导出onnx并预测
需要做点什么 方便广大烟酒生研究生.人工智障炼丹师算法工程师快速使用keras,所以特写此文章,默认使用者已有基本的深度学习概念.数据集概念. 系统环境 python 3.7.4 tensorflow ...
- 百度Paddle速查_CPU和GPU的mnist预测训练_模型导出_模型导入再预测_导出onnx并预测
需要做点什么 方便广大烟酒生研究生.人工智障炼丹师算法工程师快速使用百度PaddelPaddle,所以特写此文章,默认使用者已有基本的深度学习概念.数据集概念. 系统环境 python 3.7.4 p ...
- [深度学习] Pytorch（三）—— 多/单GPU、CPU，训练保存、加载模型参数问题
[深度学习] Pytorch(三)-- 多/单GPU.CPU,训练保存.加载预测模型问题 上一篇实践学习中,遇到了在多/单个GPU.GPU与CPU的不同环境下训练保存.加载使用使用模型的问题,如果保存 ...
- 人工智能深度学习框架MXNet实战：深度神经网络的交通标志识别训练
人工智能深度学习框架MXNet实战:深度神经网络的交通标志识别训练 MXNet 是一个轻量级.可移植.灵活的分布式深度学习框架,2017 年 1 月 23 日,该项目进入 Apache 基金会,成为 ...
- numpy（ndarray）和tensor（GPU上的numpy）速查
类型(Types) Numpy PyTorch np.ndarray torch.Tensor np.float32 torch.float32; torch.float np.float64 tor ...
- mxnet：结合R与GPU加速深度学习
转载于统计之都,http://cos.name/tag/dmlc/,作者陈天奇 ------------------------------------------------------------ ...
- CUDA 7.0 速查手册
Create by Jane/Santaizi 03:57:00 3/14/2016 All right reserved. 速查手册基于 CUDA 7.0 toolkit documentation ...
- 常用的14种HTTP状态码速查手册
分类 1xx \> Information(信息) // 接收的请求正在处理 2xx \> Success(成功) // 请求正常处理完毕 3xx \> Redirection(重定 ...
- jQuery 常用速查
jQuery 速查 基础 $("css 选择器") 选择元素,创建jquery对象 $("html字符串") 创建jquery对象 $(callback) $( ...
随机推荐
- 【SQL】数据库运维实习工作经验
1.导入表格的时候回出现类型不对应的问题,T-SQL代码如下: USE zzzj2017 ALTER TABLE CJ ALTER COLUMN 付款时间 datetime 2.删除 USE s ...
- 微信小程序商品发布
<!--pages/good/good.wxml--> <!--商品发布--> <form bindsubmit="formSubmit"> & ...
- docker学习(三) - docker理解及命令
Docker 包括三个基本概念 镜像(Image) 容器(Container) 仓库(Repository) 镜像 Docker 镜像就是一个只读的模板. 例如:一个镜像可以包含一个完整的 ubunt ...
- tensorflow源码解析之framework-resource
目录 什么是resource 如何使用resource 如何管理resource 常用resource 其它结构 关系图 涉及的文件 迭代记录 1. 什么是resource 我们知道,TF的计算是由设 ...
- yum源出现 AppStream‘ 缓存失败，忽略这个 repo。同步仓库 ‘base‘ 缓存失败，忽略这个 repo 的问题解决办法
今天安装Wget时出现如下错误 到 /etc/yum.repos.d/目录下编辑CentOS-Base.repo ,更改为如下内容. vim CentOS-Base.repo # CentOS-Bas ...
- Fiddler抓包常用功能
通过上一篇文章Fiddler移动端抓包,我们知道了Fiddler抓包原理以及怎样进行移动端抓包,接下来介绍Fiddler中常用的功能. Fiddler中常用的功能如下: 停止抓包 清空会话窗内容 过滤 ...
- WOE(weight of evidence, 证据权重)
1. WOE(weight of evidence, 证据权重) WOE是一种衡量正常样本( Good)和违约样本( Bad)分布的差异方法 WOE=ln(Distr Good/Distr Bad)例 ...
- 微信Native支付
微信Native支付对接(扫码) 由于有业务需求对接了微信和paypal支付,这边做个记录 微信支付开发文档:https://pay.weixin.qq.com/wiki/doc/api/index. ...
- CVE-2010-2861（Adobe ColdFusion 文件读取漏洞）
漏洞介绍 Adobe ColdFusion是美国Adobe公司的一款动态Web服务器产品,其运行的CFML(ColdFusion Markup Language)是针对Web应用的一种程序设计语言. ...
- idea使用技巧、心得1
0.安装idea之后的准备 (1) 永久快乐使用:在我的博客搜索安装idea关键词既可 (2) 取消更新: (3) idea 官网的关于idea的使用手册:https://www.jetbrains. ...