autoencoder

3-Neural_Network_Architecture/autoencoder.py

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+#! /usr/bin/env python
+# coding=utf-8
+#================================================================
+#   Copyright (C) 2019 * Ltd. All rights reserved.
+#
+#   Editor      : VIM
+#   File name   : autoencoder.py
+#   Author      : YunYang1994
+#   Created date: 2019-11-05 19:53:58
+#   Description :
+#
+#================================================================
+
+import random
+import numpy as np
+import tensorflow as tf
+import matplotlib.pyplot as plt
+from matplotlib import cm
+
+
+class Encoder(tf.keras.Model):
+    def __init__(self):
+        super(Encoder, self).__init__()
+        self.dense_1 = tf.keras.layers.Dense(128, activation='tanh')
+        self.dense_2 = tf.keras.layers.Dense(64 , activation='tanh')
+        self.dense_3 = tf.keras.layers.Dense(32 , activation='tanh')
+        self.dense_4 = tf.keras.layers.Dense(2  , activation='sigmoid')
+    def call(self, x, training=False):
+        out = self.dense_1(x)
+        out = self.dense_2(out)
+        out = self.dense_3(out)
+        out = self.dense_4(out)
+        return out
+
+
+class Decoder(tf.keras.Model):
+    def __init__(self):
+        super(Decoder, self).__init__()
+        self.dense_1 = tf.keras.layers.Dense(16 , activation='tanh')
+        self.dense_2 = tf.keras.layers.Dense(64 , activation='tanh')
+        self.dense_3 = tf.keras.layers.Dense(128, activation='tanh')
+        self.dense_4 = tf.keras.layers.Dense(784, activation='sigmoid')
+    def call(self, x, training=False):
+        out = self.dense_1(x)
+        out = self.dense_2(out)
+        out = self.dense_3(out)
+        out = self.dense_4(out)
+        return out
+
+class Autoencoder(tf.keras.Model):
+    def __init__(self):
+        super(Autoencoder, self).__init__()
+        self.encoder = Encoder()
+        self.decoder = Decoder()
+    def call(self, x, training=False):
+        out = self.encoder(x, training)
+        out = self.decoder(out, training)
+        return out
+
+
+(X_train, y_train), (X_test, y_test) = tf.keras.datasets.mnist.load_data()
+X_train = np.reshape(X_train, (-1, 784)) / 255.
+batch_size = 512
+optimizer = tf.keras.optimizers.Adam(lr=0.001)
+model = Autoencoder()
+sample = np.reshape(X_test[:5], (5, 784))
+
+for step in range(10000):
+    true_image = X_train[np.random.choice(X_train.shape[0], batch_size)]
+    with tf.GradientTape() as tape:
+        pred_image = model(true_image, training=True)
+        loss = tf.reduce_mean(tf.square(pred_image-true_image))
+        gradients = tape.gradient(loss, model.trainable_variables)
+        optimizer.apply_gradients(zip(gradients, model.trainable_variables))
+    if step % 200  == 0: print("=> %4d loss %.4f" %(step, loss))
+    if step % 1000 == 0:
+        pred_image = model(sample, training=False)
+        pred_image = np.reshape(pred_image, (5, 28, 28))
+        show_image = np.concatenate(pred_image[:5], -1)
+        plt.tight_layout()
+        plt.imshow(show_image)
+        plt.savefig("%d.png" %((step+1) // 2000))
+
+"""
+visualize embedding in 2D
+"""
+sample = np.reshape(X_test[:5000], (5000, 784))
+label = y_test[:5000]
+embeddings = model.encoder(sample, training=False)
+
+fig,ax = plt.subplots()
+X, Y = embeddings[:,0].numpy(), embeddings[:,1].numpy()
+ax.set_xlim(X.min(), X.max())
+ax.set_ylim(Y.min(), Y.max())
+for x,y,l in zip(X,Y,label):
+    c = cm.rainbow(int(255 *l/ 9))
+    ax.text(x, y, l, color=c)
+    # plt.plot(x,y, '.', c=c)
+plt.axis('off')
+plt.legend()
+plt.tight_layout()
+plt.savefig("embedding.png")
+plt.show()
+
+