导入 matplotlib 模块：
import matplotlib
查看自己版本所支持的backends：
print(matplotlib.rcsetup.all_backends)
返回信息：
[‘GTK3Agg‘, ‘GTK3Cairo‘, ‘MacOSX‘, ‘nbAgg‘, ‘Qt4Agg‘, ‘Qt4Cairo‘, ‘Qt5Agg‘, ‘Qt5Cairo‘, ‘TkAgg‘, ‘TkCairo‘, ‘WebAgg‘, ‘WX‘, ‘WXAgg‘, ‘WXCairo‘, ‘agg‘, ‘cairo‘, ‘pdf‘, ‘pgf‘, ‘ps‘, ‘svg‘, ‘template‘]
查看当前工作的matplotlibrc文件是哪个：
print(matplotlib.matplotlib_fname())
返回信息：
D:\ProgramData\Anaconda2\lib\site-packages\matplotlib\mpl-data\matplotlibrc
打开 matplotlibrc 查看相应内容：
将 backend 修改为 TkAgg：
执行如下代码：
import numpy as np import pandas as pd import matplotlib.pyplot as plt from keras.models import Model from keras.layers import Dense, Activation, Input, Reshape from keras.layers import Conv1D, Flatten, Dropout from keras.optimizers import SGD, Adam def sample_data(n_samples=10000, x_vals=np.arange(0, 5, .1), max_offset=100, mul_range=[1, 2]): vectors = [] for i in range(n_samples): offset = np.random.random() * max_offset mul = mul_range[0] + np.random.random() * (mul_range[1] - mul_range[0]) vectors.append( np.sin(offset + x_vals * mul) / 2 + .5 ) return np.array(vectors) ax = pd.DataFrame(np.transpose(sample_data(5))).plot() plt.show()
生成图片：
执行代码：
def get_generative(G_in, dense_dim=200, out_dim=50, lr=1e-3): x = Dense(dense_dim)(G_in) x = Activation(‘tanh‘)(x) G_out = Dense(out_dim, activation=‘tanh‘)(x) G = Model(G_in, G_out) opt = SGD(lr=lr) G.compile(loss=‘binary_crossentropy‘, optimizer=opt) return G, G_out G_in = Input(shape=[10]) G, G_out = get_generative(G_in) G.summary()
生成图像：
参考：
https://blog.csdn.net/tanmx219/article/details/88074600
https://blog.csdn.net/xqf1528399071/article/details/53385593
原文：https://www.cnblogs.com/ratels/p/11158734.html