Create vae_vmf_keras.py

vae_vmf_keras.py

@@ -0,0 +1,104 @@
+#! -*- coding: utf-8 -*-
+# vMF-VAE简单实现参考
+
+import numpy as np
+import matplotlib.pyplot as plt
+from keras.layers import Input, Dense, Lambda
+from keras.models import Model
+from keras import backend as K
+from keras.datasets import mnist
+
+# 基本参数
+batch_size = 100
+original_dim = 784
+latent_dim = 8
+intermediate_dim = 256
+epochs = 50
+kappa = 20
+
+# 加载数据集
+(x_train, y_train_), (x_test, y_test_) = mnist.load_data()
+x_train = x_train.astype('float32') / 255.
+x_test = x_test.astype('float32') / 255.
+x_train = x_train.reshape((len(x_train), np.prod(x_train.shape[1:])))
+x_test = x_test.reshape((len(x_test), np.prod(x_test.shape[1:])))
+
+# 模型定义
+x = Input(shape=(original_dim,))
+h = Dense(intermediate_dim, activation='relu')(x)
+
+# 参数mu
+mu = Dense(latent_dim)(h)
+mu = Lambda(K.l2_normalize)(mu)
+
+
+def sampling(mu):
+    """vMF分布重参数操作
+    """
+    dims = K.int_shape(mu)[-1]
+    # 预先计算一批w
+    epsilon = 1e-7
+    x = np.arange(-1 + epsilon, 1, epsilon)
+    y = kappa * x + np.log(1 - x**2) * (dims - 3) / 2
+    y = np.cumsum(np.exp(y - y.max()))
+    y = y / y[-1]
+    W = K.constant(np.interp(np.random.random(10**6), y, x))
+    # 实时采样w
+    idx = K.random_uniform(K.shape(mu[:, :1]), 0, 10**6, dtype='int32')
+    w = K.gather(W, idx)
+    # 实时采样z
+    eps = K.random_normal(K.shape(mu))
+    nu = eps - K.sum(eps * mu, axis=1, keepdims=True) * mu
+    nu = K.l2_normalize(nu)
+    return w * mu + (1 - w**2)**0.5 * nu
+
+
+# 重参数层
+z = Lambda(sampling)(mu)
+
+# 解码层
+decoder_h = Dense(intermediate_dim, activation='relu')
+decoder_mean = Dense(original_dim, activation='sigmoid')
+h_decoded = decoder_h(z)
+x_decoded_mean = decoder_mean(h_decoded)
+
+# 建立模型
+vae = Model(x, x_decoded_mean)
+
+loss = K.sum(K.binary_crossentropy(x, x_decoded_mean), axis=-1)
+vae.add_loss(K.mean(loss))
+vae.compile(optimizer='adam')
+vae.summary()
+
+vae.fit(
+    x_train,
+    shuffle=True,
+    epochs=epochs,
+    batch_size=batch_size,
+    validation_data=(x_test, None)
+)
+
+# 构建生成器
+decoder_input = Input(shape=(latent_dim,))
+_h_decoded = decoder_h(decoder_input)
+_x_decoded_mean = decoder_mean(_h_decoded)
+generator = Model(decoder_input, _x_decoded_mean)
+
+# 观察随机采样结果
+n = 15
+digit_size = 28
+figure = np.zeros((digit_size * n, digit_size * n))
+
+# 球面上均匀采样
+for i in range(n):
+    for j in range(n):
+        z_sample = np.random.randn(1, latent_dim)
+        z_sample /= (z_sample**2).sum()**0.5
+        x_decoded = generator.predict(z_sample)
+        digit = x_decoded[0].reshape(digit_size, digit_size)
+        figure[i * digit_size:(i + 1) * digit_size,
+               j * digit_size:(j + 1) * digit_size] = digit
+
+plt.figure(figsize=(10, 10))
+plt.imshow(figure, cmap='Greys_r')
+plt.savefig('test.png')