Keras速查_CPU和GPU的mnist預測訓練_模型匯出_模型匯入再預測_匯出onnx並預測

Kalafinaian發表於2022-03-20
KerasGPU模型

需要做點什麼

方便廣大菸酒生研究生、人工智障煉丹師演算法工程師快速使用keras，所以特寫此文章，預設使用者已有基本的深度學習概念、資料集概念。

系統環境

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

資料準備

MNIST資料集csv檔案是一個42000x785的矩陣
42000表示有42000張圖片
785中第一列是圖片的類別(0,1,2,..,9),第二列到最後一列是圖片資料向量 (28x28的圖片張成784的向量)， 資料集長這個樣子:

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. 匯入需要的包

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_categorical

tf.autograph.set_verbosity(0)
logging.getLogger("tensorflow").setLevel(logging.ERROR)

2. 引數準備

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"  # 沒有gpu請反註釋這行以使用CPU

if S_DEVICE == "cpu":
    os.environ["CUDA_VISIBLE_DEVICES"] = "-1"

3. 讀取資料

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)

執行輸出

(42000, 785)
(42000, 785)
(32000, 28, 28, 1)
(32000, 10)
(10000, 28, 28, 1)
(10000, 10)

4. 模型構建

x_in = Input(shape=(28, 28, 1))  # 影像維度必須是 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())

執行輸出

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. 模型訓練和儲存

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)

執行輸出

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.模型載入和載入模型使用

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])

執行輸出

load model ok
load model Test score: 0.13516278564929962
load model Test accuracy: 0.9616000056266785

7.匯出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)

執行輸出

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. 載入ONNX並執行

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執行
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])

執行輸出

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]]

你甚至不願意Start的Github

ai_fast_handbook

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