Create 07_gbrbm.py

deepLearning/07_gbrbm.py

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+"""
+Restricted Boltzmann Machines (RBM)
+author: Ye Hu
+2016/12/18
+redit：wanyouwen 2018/05/02
+"""
+import os
+import timeit
+import numpy as np
+import tensorflow as tf
+from PIL import Image
+from utils import tile_raster_images
+import input_data
+from rbm import RBM
+
+
+class GBRBM(RBM):
+    """
+    Gaussian-binary Restricted Boltzmann Machines
+    Note we assume that the standard deviation is a constant (not training parameter)
+    You better normalize you data with range of [0, 1.0].
+    """
+    def __init__(self, inpt=None, n_visiable=784, n_hidden=500, sigma=1.0, W=None,
+                 hbias=None, vbias=None, sample_visible=True):
+        """
+        :param inpt: Tensor, the input tensor [None, n_visiable]
+        :param n_visiable: int, number of visiable units
+        :param n_hidden: int, number of hidden units
+        :param sigma: float, the standard deviation (note we use the same σ for all visible units)
+        :param W, hbias, vbias: Tensor, the parameters of RBM (tf.Variable)
+        :param sample_visble: bool, if True, do gaussian sampling.
+        """
+        super(GBRBM, self).__init__(inpt, n_visiable, n_hidden, W, hbias, vbias)
+        self.sigma = sigma
+        self.sample_visible = sample_visible
+
+    @staticmethod
+    def sample_gaussian(x, sigma):
+        return x + tf.random_normal(tf.shape(x), mean=0.0, stddev=sigma)
+
+    def propdown(self, h):
+        """Compute the mean for visible units given hidden units"""
+        return tf.matmul(h, tf.transpose(self.W)) + self.vbias
+
+    def sample_v_given_h(self, h0_sample):
+        """Sampling the visiable units given hidden sample"""
+        v1_mean = self.propdown(h0_sample)
+        v1_sample = v1_mean
+        if self.sample_visible:
+            v1_sample = GBRBM.sample_gaussian(v1_mean, self.sigma)
+        return (v1_mean, v1_sample)
+
+    def propup(self, v):
+        """Compute the sigmoid activation for hidden units given visible units"""
+        return tf.nn.sigmoid(tf.matmul(v, self.W) / self.sigma**2 + self.hbias)
+
+    def free_energy(self, v_sample):
+        """Compute the free energy"""
+        wx_b = tf.matmul(v_sample, self.W) / self.sigma**2 + self.hbias
+        vbias_term = tf.reduce_sum(0.5 * tf.square(v_sample - self.vbias) / self.sigma**2, axis=1)
+        hidden_term = tf.reduce_sum(tf.log(1.0 + tf.exp(wx_b)), axis=1)
+        return -hidden_term + vbias_term
+
+    def get_reconstruction_cost(self):
+        """Compute the mse of the original input and the reconstruction"""
+        activation_h = self.propup(self.input)
+        activation_v = self.propdown(activation_h)
+        mse = tf.reduce_mean(tf.reduce_sum(tf.square(self.input - activation_v), axis=1))
+        return mse  
+
+
+
+if __name__ == "__main__":
+    # mnist examples
+    mnist = input_data.read_data_sets("MNIST_data/", one_hot=True)
+    # define input
+    x = tf.placeholder(tf.float32, shape=[None, 784])
+    # set random_seed
+    tf.set_random_seed(seed=99999)
+    np.random.seed(123)
+    # the rbm model
+    n_visiable, n_hidden = 784, 500
+    rbm = GBRBM(x, n_visiable=n_visiable, n_hidden=n_hidden)
+
+    learning_rate = 0.01
+    batch_size = 50
+    cost = rbm.get_reconstruction_cost()
+    # Create the persistent variable
+    #persistent_chain = tf.Variable(tf.zeros([batch_size, n_hidden]), dtype=tf.float32)
+    persistent_chain = None
+    train_ops = rbm.get_train_ops(learning_rate=learning_rate, k=1, persistent=persistent_chain)
+    init = tf.global_variables_initializer()
+
+    output_folder = "rbm_plots"
+    if not os.path.isdir(output_folder):
+        os.makedirs(output_folder)
+    os.chdir(output_folder)
+
+    training_epochs = 15
+    display_step = 1
+    print("Start training...")
+
+    with tf.Session() as sess:
+        start_time = timeit.default_timer()
+        sess.run(init)
+        for epoch in range(training_epochs):
+            avg_cost = 0.0
+            batch_num = int(mnist.train.num_examples / batch_size)
+            for i in range(batch_num):
+                x_batch, _ = mnist.train.next_batch(batch_size)
+                # 训练
+                sess.run(train_ops, feed_dict={x: x_batch})
+                # 计算cost
+                avg_cost += sess.run(cost, feed_dict={x: x_batch,}) / batch_num
+            # 输出
+            if epoch % display_step == 0:
+                print("Epoch {0} cost: {1}".format(epoch, avg_cost))
+            # Construct image from the weight matrix
+            image = Image.fromarray(
+            tile_raster_images(
+                X=sess.run(tf.transpose(rbm.W)),
+                img_shape=(28, 28),
+                tile_shape=(10, 10),
+                tile_spacing=(1, 1)))
+            image.save("test_filters_at_epoch_{0}.png".format(epoch))
+
+        end_time = timeit.default_timer()
+        training_time = end_time - start_time
+        print("Finished!")
+        print("  The training ran for {0} minutes.".format(training_time/60,))
+
+        # Randomly select the 'n_chains' examples
+        n_chains = 20
+        n_batch = 10
+        n_samples = n_batch*2
+        number_test_examples = mnist.test.num_examples
+        test_indexs = np.random.randint(number_test_examples - n_chains*n_batch)
+        test_samples = mnist.test.images[test_indexs:test_indexs+n_chains*n_batch]
+        image_data = np.zeros((29*(n_samples+1)+1, 29*(n_chains)-1),
+                          dtype="uint8")
+        # Add the original images
+        for i in range(n_batch):
+            image_data[2*i*29:2*i*29+28,:] = tile_raster_images(X=test_samples[i*n_batch:(i+1)*n_chains],
+                                            img_shape=(28, 28),
+                                            tile_shape=(1, n_chains),
+                                            tile_spacing=(1, 1))
+            samples = sess.run(rbm.reconstruct(x), feed_dict={x:test_samples[i*n_batch:(i+1)*n_chains]})
+            image_data[(2*i+1)*29:(2*i+1)*29+28,:] = tile_raster_images(X=samples,
+                                            img_shape=(28, 28),
+                                            tile_shape=(1, n_chains),
+                                            tile_spacing=(1, 1))
+
+        image = Image.fromarray(image_data)
+        image.save("original_and_reconstruct.png")
+
+
+