使用 28x28 bmp測試訓練後的模型 tensorflow mnist
2019/03/06 16:19
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訓練CNN/mnist好後,用以下指令保存模型及weight
model.save("05.h5")
關於BMP
24 bit bmp檔, 檔頭54 byte, 之後每個像素RGB佔用3 byte,
28x28像素 = 54 + 28x28x3 = 54+2352 = 2406 byte
用apt-get kolourpaint4可以產生bmp
讀取bmp時要注意2點
1.bmp是由下行往上行存,讀入圖檔要反向
2.bmp的白是255, mnist的圖,白是0
from keras.datasets import mnist
from keras.models import load_model
from keras.utils import np_utils
from keras.layers import Conv2D, MaxPooling2D
from keras.layers import Dense, Activation, Flatten
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import keras
import tensorflow as tf
import numpy as np
def plot_image(image):
fig=plt.gcf()
fig.set_size_inches(2,2)
plt.imshow(image,cmap=binary)
plt.show()
model = load_model("05_cnn.h5")
(X_train_image, y_train_label), (X_test_image, y_test_label) = mnist.load_data()
print(X_train_image.shape)
x_train = X_train_image.reshape(X_train_image.shape[0],28,28,1)
print(x_train.shape)
x_train = x_train.astype(float32)
x_train /= 255
y_train = keras.utils.to_categorical(y_train_label, num_classes=10)
x_test = X_test_image.reshape(X_test_image.shape[0], 28, 28, 1)
x_test = x_test.astype(float32)
x_test /= 255
y_test = keras.utils.to_categorical(y_test_label, num_classes=10)
def draw_1_28_28_1(np_ary_1_28_28_1):
for i2 in range(0,28) :
st1=""
for i3 in range(0,28) :
if np_ary_1_28_28_1[0][i2][i3][0] == 0 :
st1=st1+" "
else:
st1=st1+"*"
print(st1)
def prediction_one(test_id):
x1 = x_test[test_id:(test_id+1)]
print(x1.shape)
draw_1_28_28_1(x1)
plot_image(X_test_image[test_id]);
prediction = model.predict(x1)
answser=np.argmax(prediction[0])
return answser
for i1 in range(0,1):
print("id=",i1," p=",prediction_one(i1))
import array as ary
x3=np.zeros((28,28),dtype=float32)
print(x3.shape)
with open("./6.bmp","rb") as f_bmp1:
byte_ary = f_bmp1.read(54)
print(str(byte_ary[0:2]))
for i1 in range(0,28):
st1=""
byte_ary = f_bmp1.read(28*3)
x2=np.frombuffer(byte_ary,np.uint8,28*3,0)
#x2=x2.dtype=float32
x2=x2.reshape(28,3)
for i3 in range(0,28) :
x3[28-i1-1][i3]=255-x2[i3][0]
for i3 in range(0,28) :
if x2[i3][0] == 0 :
st1=st1+" "
else:
st1=st1+"#"
print(i1,st1)
for i2 in range(0,28) :
st1=""
for i3 in range(0,28) :
if x3[i2][i3] == 0 :
st1=st1+" "
else:
st1=st1+"*"
print(st1)
x5=x3.reshape(1,28,28,1)
x5 /=255
predi1 = model.predict(x5)
print("prediction-"np.argmax(predi1[0]))
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