将浮点数列表转换为 NumPy 数组

Question

我正在使用神经网络，我想在 FPGA 上实现它。我有一个在 MNIST 上工作的代码，我想使用 float32 获得初始权重，然后在 FPGA 上用定点重新训练权重。

我正在 python 中运行我的模拟。我正在寻找一种方法来进行这种转换

from keras.datasets import mnist
from keras.layers import Dense
from keras.models import Sequential
from keras.layers import Dropout
from keras.utils import np_utils
import matplotlib.pyplot as plt
(x, y), (X, Y) = mnist.load_data()

num = x.shape[1] * x.shape[2]
x = x.reshape(x.shape[0],x.shape[1]*x.shape[2]).astype('float32')
X = X.reshape(X.shape[0],X.shape[1]*X.shape[2]).astype('float32')

x = x/255
X = X/255

y = np_utils.to_categorical(y)
Y = np_utils.to_categorical(Y)

classes = y.shape[1]

def calc():
    model = Sequential()
    model.add(Dense(num, input_dim = num, init = 'normal', activation = 'relu'))
    model.add(Dense(classes, init = 'normal', activation = 'softmax'))
    model.compile(loss = 'categorical_crossentropy', optimizer = 'adam', metrics = ['accuracy'])
    return model

model = calc()
model.fit(x, y, validation_data=(X, Y), nb_epoch=10, batch_size=200,
    verbose=2)
scores = model.evaluate(X, Y, verbose=0)
print("Accuracy: ", scores)

Answer 1

将您的列表传递给 np.array 和 dtype=np.float32 以指定 32 位浮点数作为数据类型：

np.array(your_list, dtype=np.float32)