Method fit

unsupervised_class3/dcgan_tf.py:407–496

(self, X)

  def fit(self, X):
    d_costs = []
    g_costs = []

    N = len(X)
    n_batches = N // BATCH_SIZE
    total_iters = 0
    for i in range(EPOCHS):
      print("epoch:", i)
      np.random.shuffle(X)
      for j in range(n_batches):
        t0 = datetime.now()

        if type(X[0]) is str:
          # is celeb dataset
          batch = util.files2images(
            X[j*BATCH_SIZE:(j+1)*BATCH_SIZE]
          )

        else:
          # is mnist dataset
          batch = X[j*BATCH_SIZE:(j+1)*BATCH_SIZE]

        Z = np.random.uniform(-1, 1, size=(BATCH_SIZE, self.latent_dims))

        # train the discriminator
        _, d_cost, d_acc = self.sess.run(
          (self.d_train_op, self.d_cost, self.d_accuracy),
          feed_dict={self.X: batch, self.Z: Z, self.batch_sz: BATCH_SIZE},
        )
        d_costs.append(d_cost)

        # train the generator
        _, g_cost1 = self.sess.run(
          (self.g_train_op, self.g_cost),
          feed_dict={self.Z: Z, self.batch_sz: BATCH_SIZE},
        )
        # g_costs.append(g_cost1)
        _, g_cost2 = self.sess.run(
          (self.g_train_op, self.g_cost),
          feed_dict={self.Z: Z, self.batch_sz: BATCH_SIZE},
        )
        g_costs.append((g_cost1 + g_cost2)/2) # just use the avg

        print("  batch: %d/%d  -  dt: %s - d_acc: %.2f" % (j+1, n_batches, datetime.now() - t0, d_acc))


        # save samples periodically
        total_iters += 1
        if total_iters % SAVE_SAMPLE_PERIOD == 0:
          print("saving a sample...")
          samples = self.sample(64) # shape is (64, D, D, color)

          # for convenience
          d = self.img_length
          
          if samples.shape[-1] == 1:
            # if color == 1, we want a 2-D image (N x N)