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基于树莓派的自动驾驶小车,利用树莓派和Tensorflow实现小车在赛道的自动驾驶 spread retract

https://github.com/Timthony/self_drive

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keras_mnist_cnn_steps.py 2.04 KB
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tianhangz authored 2018-11-05 18:27 . 修改版
import keras
from keras.datasets import mnist
from keras.models import Sequential
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
import matplotlib.pyplot as plt
# step1
(x_train, y_train), (x_test, y_test) = mnist.load_data()
plt.figure()
plt.imshow(x_train[30], cmap='gray')
plt.show()
print(y_train.shape)
print(y_train[30])
# step2
img_rows, img_cols = 28, 28
x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1)
x_test = x_test.reshape(x_test.shape[0], img_rows, img_cols, 1)
input_shape = (img_rows, img_cols, 1)
# step3
x_train = x_train.astype('float32')
x_test = x_test.astype('float32')
x_train /= 255
x_test /= 255
print('x_train shape:', x_train.shape)
print(x_train.shape[0], 'train samples')
print(x_test.shape[0], 'test samples')
# step4
num_classes = 10
y_train = keras.utils.to_categorical(y_train, num_classes)
y_test = keras.utils.to_categorical(y_test, num_classes)
# step5
model = Sequential()
model.add(Conv2D(32, kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
model.add(Conv2D(64, (3, 3), activation='relu'))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Dropout(0.25))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
# step6
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adadelta(),
metrics=['accuracy'])
# step7
model.fit(x_train, y_train,
batch_size=128,
epochs=12,
verbose=1,
validation_data=(x_test, y_test))
# step8
score = model.evaluate(x_test, y_test, verbose=1)
print('Test loss:', score[0])
print('Test accuracy:', score[1])
# step9
prediction = model.predict(x_test[20].reshape(1,28,28,1))
prediction = prediction.argmax(axis=1)
plt.figure()
plt.imshow(x_test[20].reshape(28,28))
plt.text(0,-3,prediction)
plt.show()
# step10
model.save('my_model.h5')

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