如何使用Keras在CPU和GPU上训练mnist模型，导出为onnx格式并再次预测？
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本文共计1588个文字，预计阅读时间需要7分钟。
需要做些什么+方便广大+酒厂生+研究生、+人工智能工程师+算法工程师快速使用keras，特编写此文章。默认读者已有基本的深度学习、数据集概念。系统环境+python 3.7.4+tensorflow。
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。
python 3.7.4
tensorflow 2.6.0
keras 2.6.0
onnx 1.9.0
onnxruntime-gpu 1.9.0
tf2onnx 1.9.3
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
4 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
import os
import onnx
import keras
import logging
import subprocess
import numpy as np
import pandas as pd
import tensorflow as tf
import onnxruntime as ort
from sklearn.metrics import accuracy_score
from keras.models import Sequential, Model, load_model, save_model
from keras.layers import Dense, Activation, Dropout, Conv2D, Flatten, MaxPool2D, Input, Conv1D
from keras.utils.np_utils import to_categorical
tf.autograph.set_verbosity(0)
logging.getLogger("tensorflow").setLevel(logging.ERROR)
2. 参数准备
N_EPOCH = 1
N_BATCH = 64
N_BATCH_NUM = 500
S_DATA_PATH = r"mnist_train.csv"
S_KERAS_MODEL_DIR_PATH = r"cnn_keras"
S_KERAS_MODEL_PATH = r"cnn_keras.h5"
S_ONNX_MODEL_PATH = r"cnn_keras.onnx"
S_DEVICE, N_DEVICE_ID, S_DEVICE_FULL = "cuda", 0, "cuda:0" # 使用gpu
# S_DEVICE, N_DEVICE_ID, S_DEVICE_FULL = "cpu", 0, "cpu" # 没有gpu请反注释这行以使用CPU
if S_DEVICE == "cpu":
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
3. 读取数据
df = pd.read_csv(S_DATA_PATH, header=None)
np_mat = np.array(df)
print(df.shape)
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], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
Y_train = to_categorical(Y_train, num_classes=10)
Y_test = to_categorical(Y_test, num_classes=10)
print(X_train.shape)
print(Y_train.shape)
print(X_test.shape)
print(Y_test.shape)
运行输出
(42000, 785)
(42000, 785)
(32000, 28, 28, 1)
(32000, 10)
(10000, 28, 28, 1)
(10000, 10)
4. 模型构建
x_in = Input(shape=(28, 28, 1)) # 图像维度必须是 w h c
x = Conv2D(filters=32, kernel_size=(3, 3))(x_in)
x = MaxPool2D(pool_size=(2, 2))(x)
x = Dropout(0.2)(x)
x = Flatten()(x)
x = Dense(128)(x)
x = Activation('relu')(x)
x = Dense(10)(x)
y = Activation('softmax')(x)
model = Model(x_in, y)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
print(model.summary())
运行输出
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 28, 28, 1)] 0
_________________________________________________________________
conv2d (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 32) 0
_________________________________________________________________
dropout (Dropout) (None, 13, 13, 32) 0
_________________________________________________________________
flatten (Flatten) (None, 5408) 0
_________________________________________________________________
dense (Dense) (None, 128) 692352
_________________________________________________________________
activation (Activation) (None, 128) 0
_________________________________________________________________
dense_1 (Dense) (None, 10) 1290
_________________________________________________________________
activation_1 (Activation) (None, 10) 0
=================================================================
Total params: 693,962
Trainable params: 693,962
Non-trainable params: 0
_________________________________________________________________
None
5. 模型训练和保存
model.fit(X_train,
Y_train,
epochs=N_EPOCH,
batch_size=N_BATCH,
verbose=1,
validation_data=(X_test, Y_test))
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
save_model(model, S_KERAS_MODEL_PATH)
运行输出
486/500 [============================>.] - ETA: 0s - loss: 0.2873 - accuracy: 0.9144
500/500 [==============================] - 4s 3ms/step - loss: 0.2837 - accuracy: 0.9155 - val_loss: 0.1352 - val_accuracy: 0.9616
Test score: 0.13516278564929962
Test accuracy: 0.9616000056266785
6.模型加载和加载模型使用
load_model = load_model(S_KERAS_MODEL_PATH)
print("load model ok")
score = load_model.evaluate(X_test, Y_test, verbose=0)
print('load model Test score:', score[0])
print('load model Test accuracy:', score[1])
运行输出
load model ok
load model Test score: 0.13516278564929962
load model Test accuracy: 0.9616000056266785
7.导出ONNX
s_cmd = 'python -m tf2onnx.convert --keras %s --output %s' % (S_KERAS_MODEL_PATH, S_ONNX_MODEL_PATH)
print(s_cmd)
print(os.system(s_cmd))
# proc = subprocess.run(s_cmd.split(), check=True)
# print(proc.returncode)
运行输出
python -m tf2onnx.convert --keras G:\Data\task_model_out\_tmp_out\cnn_keras.h5 --output G:\Data\task_model_out\_tmp_out\cnn_keras.onnx
0
8. 加载ONNX并运行
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])
运行输出
None
graph tf2onnx (
%input_1:0[FLOAT, unk__17x28x28x1]
) initializers (
%new_shape__15[INT64, 4]
%model/dense_1/MatMul/ReadVariableOp:0[FLOAT, 128x10]
%model/dense_1/BiasAdd/ReadVariableOp:0[FLOAT, 10]
%model/dense/MatMul/ReadVariableOp:0[FLOAT, 5408x128]
%model/dense/BiasAdd/ReadVariableOp:0[FLOAT, 128]
%model/conv2d/Conv2D/ReadVariableOp:0[FLOAT, 32x1x3x3]
%model/conv2d/BiasAdd/ReadVariableOp:0[FLOAT, 32]
%const_fold_opt__16[INT64, 2]
) {
%model/conv2d/BiasAdd__6:0 = Reshape(%input_1:0, %new_shape__15)
%model/conv2d/BiasAdd:0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], strides = [1, 1]](%model/conv2d/BiasAdd__6:0, %model/conv2d/Conv2D/ReadVariableOp:0, %model/conv2d/BiasAdd/ReadVariableOp:0)
%model/max_pooling2d/MaxPool:0 = MaxPool[kernel_shape = [2, 2], strides = [2, 2]](%model/conv2d/BiasAdd:0)
%model/max_pooling2d/MaxPool__12:0 = Transpose[perm = [0, 2, 3, 1]](%model/max_pooling2d/MaxPool:0)
%model/flatten/Reshape:0 = Reshape(%model/max_pooling2d/MaxPool__12:0, %const_fold_opt__16)
%model/dense/MatMul:0 = MatMul(%model/flatten/Reshape:0, %model/dense/MatMul/ReadVariableOp:0)
%model/dense/BiasAdd:0 = Add(%model/dense/MatMul:0, %model/dense/BiasAdd/ReadVariableOp:0)
%model/activation/Relu:0 = Relu(%model/dense/BiasAdd:0)
%model/dense_1/MatMul:0 = MatMul(%model/activation/Relu:0, %model/dense_1/MatMul/ReadVariableOp:0)
%model/dense_1/BiasAdd:0 = Add(%model/dense_1/MatMul:0, %model/dense_1/BiasAdd/ReadVariableOp:0)
%Identity:0 = Softmax[axis = 1](%model/dense_1/BiasAdd:0)
return %Identity:0
}
input name ['input_1:0']
output name ['Identity:0']
val device cuda
val shape [128, 28, 28, 1]
val data type tensor(float)
is_tensor True
array_equal True
providers CUDAExecutionProvider
sess env ['CUDAExecutionProvider', 'CPUExecutionProvider']
<class 'list'>
[[1.0287621e-04 9.9524093e-01 5.0408958e-04 ... 6.5664819e-05
3.8182980e-03 1.2303158e-05]
[9.9932754e-01 2.7173186e-08 3.5315077e-04 ... 3.0959238e-06
8.5986117e-05 3.6047477e-06]
[1.1101285e-05 9.9719965e-01 3.8205151e-04 ... 1.2267688e-03
7.8595197e-04 4.0839368e-05]
...
[2.8337089e-02 1.5399084e-05 2.1733245e-01 ... 1.5945830e-05
2.1134425e-02 1.7111158e-03]
[1.7888090e-06 3.3868539e-06 5.2631256e-04 ... 9.9888057e-01
5.4794059e-06 5.5255485e-04]
[4.1398227e-05 1.0462944e-06 5.5901739e-03 ... 3.1221823e-09
6.6847453e-04 7.8918066e-07]]
你甚至不愿意Start的Github
ai_fast_handbook
本文共计1588个文字，预计阅读时间需要7分钟。
需要做些什么+方便广大+酒厂生+研究生、+人工智能工程师+算法工程师快速使用keras，特编写此文章。默认读者已有基本的深度学习、数据集概念。系统环境+python 3.7.4+tensorflow。
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。
python 3.7.4
tensorflow 2.6.0
keras 2.6.0
onnx 1.9.0
onnxruntime-gpu 1.9.0
tf2onnx 1.9.3
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
4 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
7 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0
5 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0
8 0 0 0 0 0 0 0 0 0
9 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0
3 0 0 0 0 0 0 0 0 0
1 0 0 0 0 0 0 0 0 0
2 0 0 0 0 0 0 0 0 0
0 0 0 0 0 0 0 0 0 0
import os
import onnx
import keras
import logging
import subprocess
import numpy as np
import pandas as pd
import tensorflow as tf
import onnxruntime as ort
from sklearn.metrics import accuracy_score
from keras.models import Sequential, Model, load_model, save_model
from keras.layers import Dense, Activation, Dropout, Conv2D, Flatten, MaxPool2D, Input, Conv1D
from keras.utils.np_utils import to_categorical
tf.autograph.set_verbosity(0)
logging.getLogger("tensorflow").setLevel(logging.ERROR)
2. 参数准备
N_EPOCH = 1
N_BATCH = 64
N_BATCH_NUM = 500
S_DATA_PATH = r"mnist_train.csv"
S_KERAS_MODEL_DIR_PATH = r"cnn_keras"
S_KERAS_MODEL_PATH = r"cnn_keras.h5"
S_ONNX_MODEL_PATH = r"cnn_keras.onnx"
S_DEVICE, N_DEVICE_ID, S_DEVICE_FULL = "cuda", 0, "cuda:0" # 使用gpu
# S_DEVICE, N_DEVICE_ID, S_DEVICE_FULL = "cpu", 0, "cpu" # 没有gpu请反注释这行以使用CPU
if S_DEVICE == "cpu":
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
3. 读取数据
df = pd.read_csv(S_DATA_PATH, header=None)
np_mat = np.array(df)
print(df.shape)
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], 28, 28, 1)
X_test = X_test.reshape(X_test.shape[0], 28, 28, 1)
Y_train = to_categorical(Y_train, num_classes=10)
Y_test = to_categorical(Y_test, num_classes=10)
print(X_train.shape)
print(Y_train.shape)
print(X_test.shape)
print(Y_test.shape)
运行输出
(42000, 785)
(42000, 785)
(32000, 28, 28, 1)
(32000, 10)
(10000, 28, 28, 1)
(10000, 10)
4. 模型构建
x_in = Input(shape=(28, 28, 1)) # 图像维度必须是 w h c
x = Conv2D(filters=32, kernel_size=(3, 3))(x_in)
x = MaxPool2D(pool_size=(2, 2))(x)
x = Dropout(0.2)(x)
x = Flatten()(x)
x = Dense(128)(x)
x = Activation('relu')(x)
x = Dense(10)(x)
y = Activation('softmax')(x)
model = Model(x_in, y)
model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])
print(model.summary())
运行输出
Model: "model"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_1 (InputLayer) [(None, 28, 28, 1)] 0
_________________________________________________________________
conv2d (Conv2D) (None, 26, 26, 32) 320
_________________________________________________________________
max_pooling2d (MaxPooling2D) (None, 13, 13, 32) 0
_________________________________________________________________
dropout (Dropout) (None, 13, 13, 32) 0
_________________________________________________________________
flatten (Flatten) (None, 5408) 0
_________________________________________________________________
dense (Dense) (None, 128) 692352
_________________________________________________________________
activation (Activation) (None, 128) 0
_________________________________________________________________
dense_1 (Dense) (None, 10) 1290
_________________________________________________________________
activation_1 (Activation) (None, 10) 0
=================================================================
Total params: 693,962
Trainable params: 693,962
Non-trainable params: 0
_________________________________________________________________
None
5. 模型训练和保存
model.fit(X_train,
Y_train,
epochs=N_EPOCH,
batch_size=N_BATCH,
verbose=1,
validation_data=(X_test, Y_test))
score = model.evaluate(X_test, Y_test, verbose=0)
print('Test score:', score[0])
print('Test accuracy:', score[1])
save_model(model, S_KERAS_MODEL_PATH)
运行输出
486/500 [============================>.] - ETA: 0s - loss: 0.2873 - accuracy: 0.9144
500/500 [==============================] - 4s 3ms/step - loss: 0.2837 - accuracy: 0.9155 - val_loss: 0.1352 - val_accuracy: 0.9616
Test score: 0.13516278564929962
Test accuracy: 0.9616000056266785
6.模型加载和加载模型使用
load_model = load_model(S_KERAS_MODEL_PATH)
print("load model ok")
score = load_model.evaluate(X_test, Y_test, verbose=0)
print('load model Test score:', score[0])
print('load model Test accuracy:', score[1])
运行输出
load model ok
load model Test score: 0.13516278564929962
load model Test accuracy: 0.9616000056266785
7.导出ONNX
s_cmd = 'python -m tf2onnx.convert --keras %s --output %s' % (S_KERAS_MODEL_PATH, S_ONNX_MODEL_PATH)
print(s_cmd)
print(os.system(s_cmd))
# proc = subprocess.run(s_cmd.split(), check=True)
# print(proc.returncode)
运行输出
python -m tf2onnx.convert --keras G:\Data\task_model_out\_tmp_out\cnn_keras.h5 --output G:\Data\task_model_out\_tmp_out\cnn_keras.onnx
0
8. 加载ONNX并运行
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])
运行输出
None
graph tf2onnx (
%input_1:0[FLOAT, unk__17x28x28x1]
) initializers (
%new_shape__15[INT64, 4]
%model/dense_1/MatMul/ReadVariableOp:0[FLOAT, 128x10]
%model/dense_1/BiasAdd/ReadVariableOp:0[FLOAT, 10]
%model/dense/MatMul/ReadVariableOp:0[FLOAT, 5408x128]
%model/dense/BiasAdd/ReadVariableOp:0[FLOAT, 128]
%model/conv2d/Conv2D/ReadVariableOp:0[FLOAT, 32x1x3x3]
%model/conv2d/BiasAdd/ReadVariableOp:0[FLOAT, 32]
%const_fold_opt__16[INT64, 2]
) {
%model/conv2d/BiasAdd__6:0 = Reshape(%input_1:0, %new_shape__15)
%model/conv2d/BiasAdd:0 = Conv[dilations = [1, 1], group = 1, kernel_shape = [3, 3], strides = [1, 1]](%model/conv2d/BiasAdd__6:0, %model/conv2d/Conv2D/ReadVariableOp:0, %model/conv2d/BiasAdd/ReadVariableOp:0)
%model/max_pooling2d/MaxPool:0 = MaxPool[kernel_shape = [2, 2], strides = [2, 2]](%model/conv2d/BiasAdd:0)
%model/max_pooling2d/MaxPool__12:0 = Transpose[perm = [0, 2, 3, 1]](%model/max_pooling2d/MaxPool:0)
%model/flatten/Reshape:0 = Reshape(%model/max_pooling2d/MaxPool__12:0, %const_fold_opt__16)
%model/dense/MatMul:0 = MatMul(%model/flatten/Reshape:0, %model/dense/MatMul/ReadVariableOp:0)
%model/dense/BiasAdd:0 = Add(%model/dense/MatMul:0, %model/dense/BiasAdd/ReadVariableOp:0)
%model/activation/Relu:0 = Relu(%model/dense/BiasAdd:0)
%model/dense_1/MatMul:0 = MatMul(%model/activation/Relu:0, %model/dense_1/MatMul/ReadVariableOp:0)
%model/dense_1/BiasAdd:0 = Add(%model/dense_1/MatMul:0, %model/dense_1/BiasAdd/ReadVariableOp:0)
%Identity:0 = Softmax[axis = 1](%model/dense_1/BiasAdd:0)
return %Identity:0
}
input name ['input_1:0']
output name ['Identity:0']
val device cuda
val shape [128, 28, 28, 1]
val data type tensor(float)
is_tensor True
array_equal True
providers CUDAExecutionProvider
sess env ['CUDAExecutionProvider', 'CPUExecutionProvider']
<class 'list'>
[[1.0287621e-04 9.9524093e-01 5.0408958e-04 ... 6.5664819e-05
3.8182980e-03 1.2303158e-05]
[9.9932754e-01 2.7173186e-08 3.5315077e-04 ... 3.0959238e-06
8.5986117e-05 3.6047477e-06]
[1.1101285e-05 9.9719965e-01 3.8205151e-04 ... 1.2267688e-03
7.8595197e-04 4.0839368e-05]
...
[2.8337089e-02 1.5399084e-05 2.1733245e-01 ... 1.5945830e-05
2.1134425e-02 1.7111158e-03]
[1.7888090e-06 3.3868539e-06 5.2631256e-04 ... 9.9888057e-01
5.4794059e-06 5.5255485e-04]
[4.1398227e-05 1.0462944e-06 5.5901739e-03 ... 3.1221823e-09
6.6847453e-04 7.8918066e-07]]
你甚至不愿意Start的Github
ai_fast_handbook