Tips:

当前人工智能还未达到人类 5 岁水平，不过在感知方
面进步飞快。未来在机器语音、视觉识别领域，五到十
年内超越人类没有悬念。−沈向洋

代码

import os

os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
import matplotlib.pyplot as plt
# 导入数据集生成工具
import numpy as np
import seaborn as sns
from sklearn.datasets import make_moons
from sklearn.model_selection import train_test_split
from tensorflow.keras import layers, Sequential

plt.rcParams['font.size'] = 16
plt.rcParams['font.family'] = ['STKaiti']
plt.rcParams['axes.unicode_minus'] = False

OUTPUT_DIR = 'output_dir'
N_EPOCHS = 500


def load_dataset():
    # 采样点数
    N_SAMPLES = 1000
    # 测试数量比率
    TEST_SIZE = None

    X, y = make_moons(n_samples=N_SAMPLES, noise=0.25, random_state=100)
    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=TEST_SIZE, random_state=42)
    return X, y, X_train, X_test, y_train, y_test


def make_plot(X, y, plot_name, file_name, XX=None, YY=None, preds=None, dark=False, output_dir=OUTPUT_DIR):
    if dark:
        plt.style.use('dark_background')
    else:
        sns.set_style("whitegrid")
    axes = plt.gca()
    axes.set_xlim([-2, 3])
    axes.set_ylim([-1.5, 2])
    axes.set(xlabel="$x_1$", ylabel="$x_2$")
    plt.title(plot_name, fontsize=20, fontproperties='SimHei')
    plt.subplots_adjust(left=0.20)
    plt.subplots_adjust(right=0.80)
    # 根据网络输出绘制预测曲面
    if (XX is not None and YY is not None and preds is not None):
        plt.contourf(XX, YY, preds.reshape(XX.shape), 25, alpha=0.08, cmap=plt.cm.Spectral)
        plt.contour(XX, YY, preds.reshape(XX.shape), levels=[.5], cmap="Greys", vmin=0, vmax=.6)
    # 绘制正负样本
    markers = ['o' if i == 1 else 's' for i in y.ravel()]
    mscatter(X[:, 0], X[:, 1], c=y.ravel(), s=20, cmap=plt.cm.Spectral, edgecolors='none', m=markers, ax=axes)
    # plt.show()
    # 保存矢量图
    plt.savefig(file_name)


def mscatter(x, y, ax=None, m=None, **kw):
    import matplotlib.markers as mmarkers
    if not ax: ax = plt.gca()
    sc = ax.scatter(x, y, **kw)
    if (m is not None) and (len(m) == len(x)):
        paths = []
        for marker in m:
            if isinstance(marker, mmarkers.MarkerStyle):
                marker_obj = marker
            else:
                marker_obj = mmarkers.MarkerStyle(marker)
            path = marker_obj.get_path().transformed(
                marker_obj.get_transform())
            paths.append(path)
        sc.set_paths(paths)
    return sc


def network_layers_influence(X_train, y_train):
    # 构建 5 种不同层数的网络
    for n in range(5):
        # 创建容器
        model = Sequential()
        # 创建第一层
        model.add(layers.Dense(8, input_dim=2, activation='relu'))
        # 添加 n 层，共 n+2 层
        for _ in range(n):
            model.add(layers.Dense(32, activation='relu'))
        # 创建最末层
        model.add(layers.Dense(1, activation='sigmoid'))
        # 模型装配与训练
        model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
        model.fit(X_train, y_train, epochs=N_EPOCHS, verbose=1)
        # 绘制不同层数的网络决策边界曲线
        # 可视化的 x 坐标范围为[-2, 3]
        xx = np.arange(-2, 3, 0.01)
        # 可视化的 y 坐标范围为[-1.5, 2]
        yy = np.arange(-1.5, 2, 0.01)
        # 生成 x-y 平面采样网格点，方便可视化
        XX, YY = np.meshgrid(xx, yy)
        preds = model.predict_classes(np.c_[XX.ravel(), YY.ravel()])
        title = "网络层数：{0}".format(2 + n)
        file = "网络容量_%i.png" % (2 + n)
        make_plot(X_train, y_train, title, file, XX, YY, preds, output_dir=OUTPUT_DIR + '/network_layers')


def main():
    X, y, X_train, X_test, y_train, y_test = load_dataset()
    # 绘制数据集分布
    make_plot(X, y, None, "月牙形状二分类数据集分布.svg")
    # 网络层数的影响
    network_layers_influence(X_train, y_train)


if __name__ == '__main__':
    main()

标签：实战,None,plt,多层,XX,train,拟合,test,import
来源： https://www.cnblogs.com/lanercifang/p/16574139.html