Improvements to basic_conv_gen and autoencoder hparams.

PiperOrigin-RevId: 191776372

tensor2tensor/models/research/autoencoders.py

@@ -316,8 +316,8 @@ def basic_discrete_autoencoder():
   hparams = basic.basic_autoencoder()
   hparams.num_hidden_layers = 5
   hparams.hidden_size = 64
-  hparams.bottleneck_size = 2048
-  hparams.bottleneck_noise = 0.2
+  hparams.bottleneck_size = 4096
+  hparams.bottleneck_noise = 0.1
   hparams.bottleneck_warmup_steps = 3000
   hparams.add_hparam("discretize_warmup_steps", 5000)
   return hparams
@@ -327,8 +327,8 @@ def basic_discrete_autoencoder():
 def residual_discrete_autoencoder():
   """Residual discrete autoencoder model."""
   hparams = residual_autoencoder()
-  hparams.bottleneck_size = 2048
-  hparams.bottleneck_noise = 0.2
+  hparams.bottleneck_size = 4096
+  hparams.bottleneck_noise = 0.1
   hparams.bottleneck_warmup_steps = 3000
   hparams.add_hparam("discretize_warmup_steps", 5000)
   hparams.add_hparam("bottleneck_kind", "tanh_discrete")
@@ -344,7 +344,6 @@ def residual_discrete_autoencoder_big():
   hparams = residual_discrete_autoencoder()
   hparams.hidden_size = 128
   hparams.max_hidden_size = 4096
-  hparams.bottleneck_size = 8192
   hparams.bottleneck_noise = 0.1
   hparams.dropout = 0.1
   hparams.residual_dropout = 0.4

tensor2tensor/models/research/basic_conv_gen.py

@@ -40,26 +40,33 @@ def body(self, features):
     # Concat frames and down-stride.
     cur_frame = tf.to_float(features["inputs"])
     prev_frame = tf.to_float(features["inputs_prev"])
-    frames = tf.concat([cur_frame, prev_frame], axis=-1)
-    x = tf.layers.conv2d(frames, filters, kernel2, activation=tf.nn.relu,
-                         strides=(2, 2), padding="SAME")
+    x = tf.concat([cur_frame, prev_frame], axis=-1)
+    for _ in xrange(hparams.num_compress_steps):
+      x = tf.layers.conv2d(x, filters, kernel2, activation=common_layers.belu,
+                           strides=(2, 2), padding="SAME")
+      x = common_layers.layer_norm(x)
+      filters *= 2
     # Add embedded action.
-    action = tf.reshape(features["action"], [-1, 1, 1, filters])
-    x = tf.concat([x, action + tf.zeros_like(x)], axis=-1)
+    action = tf.reshape(features["action"], [-1, 1, 1, hparams.hidden_size])
+    zeros = tf.zeros(common_layers.shape_list(x)[:-1] + [hparams.hidden_size])
+    x = tf.concat([x, action + zeros], axis=-1)
 
     # Run a stack of convolutions.
     for i in xrange(hparams.num_hidden_layers):
       with tf.variable_scope("layer%d" % i):
-        y = tf.layers.conv2d(x, 2 * filters, kernel1, activation=tf.nn.relu,
+        y = tf.layers.conv2d(x, filters, kernel1, activation=common_layers.belu,
                              strides=(1, 1), padding="SAME")
         if i == 0:
           x = y
         else:
           x = common_layers.layer_norm(x + y)
     # Up-convolve.
-    x = tf.layers.conv2d_transpose(
-        x, filters, kernel2, activation=tf.nn.relu,
-        strides=(2, 2), padding="SAME")
+    for _ in xrange(hparams.num_compress_steps):
+      filters //= 2
+      x = tf.layers.conv2d_transpose(
+          x, filters, kernel2, activation=common_layers.belu,
+          strides=(2, 2), padding="SAME")
+      x = common_layers.layer_norm(x)
 
     # Reward prediction.
     reward_pred_h1 = tf.reduce_mean(x, axis=[1, 2], keep_dims=True)
@@ -78,7 +85,7 @@ def basic_conv():
   hparams = common_hparams.basic_params1()
   hparams.hidden_size = 64
   hparams.batch_size = 8
-  hparams.num_hidden_layers = 2
+  hparams.num_hidden_layers = 3
   hparams.optimizer = "Adam"
   hparams.learning_rate_constant = 0.0002
   hparams.learning_rate_warmup_steps = 500
@@ -87,6 +94,7 @@ def basic_conv():
   hparams.initializer = "uniform_unit_scaling"
   hparams.initializer_gain = 1.0
   hparams.weight_decay = 0.0
+  hparams.add_hparam("num_compress_steps", 2)
   return hparams