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 Keras速查_CPU和GPU的mnist预测训练

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 2.6.0
keras 2.6.0
onnx 1.9.0
onnxruntime-gpu 1.9.0
tf2onnx 1.9.3
数据准备 【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】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
1. 导入需要的包

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_categoricaltf.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CPUif 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 ShapeParam # ================================================================= 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=https://www.it610.com/article/(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 devicecuda val shape[128, 28, 28, 1] val data typetensor(float) is_tensorTrue array_equalTrue providersCUDAExecutionProvider sess env['CUDAExecutionProvider', 'CPUExecutionProvider'] [[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]]

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