From c57528573ea695b19cd03e98dae48f0082fb2b5e Mon Sep 17 00:00:00 2001
From: tanzhenyu <tanzheny@google.com>
Date: Thu, 27 Jun 2019 10:12:38 -0700
Subject: [PATCH] Remove model def from deepq. (#946)

---
 baselines/deepq/models.py | 95 ---------------------------------------
 1 file changed, 95 deletions(-)

diff --git a/baselines/deepq/models.py b/baselines/deepq/models.py
index 8416a24..2e670af 100644
--- a/baselines/deepq/models.py
+++ b/baselines/deepq/models.py
@@ -2,101 +2,6 @@ import tensorflow as tf
 import tensorflow.contrib.layers as layers
 
 
-def _mlp(hiddens, input_, num_actions, scope, reuse=False, layer_norm=False):
-    with tf.variable_scope(scope, reuse=reuse):
-        out = input_
-        for hidden in hiddens:
-            out = layers.fully_connected(out, num_outputs=hidden, activation_fn=None)
-            if layer_norm:
-                out = layers.layer_norm(out, center=True, scale=True)
-            out = tf.nn.relu(out)
-        q_out = layers.fully_connected(out, num_outputs=num_actions, activation_fn=None)
-        return q_out
-
-
-def mlp(hiddens=[], layer_norm=False):
-    """This model takes as input an observation and returns values of all actions.
-
-    Parameters
-    ----------
-    hiddens: [int]
-        list of sizes of hidden layers
-    layer_norm: bool
-        if true applies layer normalization for every layer
-        as described in https://arxiv.org/abs/1607.06450
-
-    Returns
-    -------
-    q_func: function
-        q_function for DQN algorithm.
-    """
-    return lambda *args, **kwargs: _mlp(hiddens, layer_norm=layer_norm, *args, **kwargs)
-
-
-def _cnn_to_mlp(convs, hiddens, dueling, input_, num_actions, scope, reuse=False, layer_norm=False):
-    with tf.variable_scope(scope, reuse=reuse):
-        out = input_
-        with tf.variable_scope("convnet"):
-            for num_outputs, kernel_size, stride in convs:
-                out = layers.convolution2d(out,
-                                           num_outputs=num_outputs,
-                                           kernel_size=kernel_size,
-                                           stride=stride,
-                                           activation_fn=tf.nn.relu)
-        conv_out = layers.flatten(out)
-        with tf.variable_scope("action_value"):
-            action_out = conv_out
-            for hidden in hiddens:
-                action_out = layers.fully_connected(action_out, num_outputs=hidden, activation_fn=None)
-                if layer_norm:
-                    action_out = layers.layer_norm(action_out, center=True, scale=True)
-                action_out = tf.nn.relu(action_out)
-            action_scores = layers.fully_connected(action_out, num_outputs=num_actions, activation_fn=None)
-
-        if dueling:
-            with tf.variable_scope("state_value"):
-                state_out = conv_out
-                for hidden in hiddens:
-                    state_out = layers.fully_connected(state_out, num_outputs=hidden, activation_fn=None)
-                    if layer_norm:
-                        state_out = layers.layer_norm(state_out, center=True, scale=True)
-                    state_out = tf.nn.relu(state_out)
-                state_score = layers.fully_connected(state_out, num_outputs=1, activation_fn=None)
-            action_scores_mean = tf.reduce_mean(action_scores, 1)
-            action_scores_centered = action_scores - tf.expand_dims(action_scores_mean, 1)
-            q_out = state_score + action_scores_centered
-        else:
-            q_out = action_scores
-        return q_out
-
-
-def cnn_to_mlp(convs, hiddens, dueling=False, layer_norm=False):
-    """This model takes as input an observation and returns values of all actions.
-
-    Parameters
-    ----------
-    convs: [(int, int, int)]
-        list of convolutional layers in form of
-        (num_outputs, kernel_size, stride)
-    hiddens: [int]
-        list of sizes of hidden layers
-    dueling: bool
-        if true double the output MLP to compute a baseline
-        for action scores
-    layer_norm: bool
-        if true applies layer normalization for every layer
-        as described in https://arxiv.org/abs/1607.06450
-
-    Returns
-    -------
-    q_func: function
-        q_function for DQN algorithm.
-    """
-
-    return lambda *args, **kwargs: _cnn_to_mlp(convs, hiddens, dueling, layer_norm=layer_norm, *args, **kwargs)
-
-
-
 def build_q_func(network, hiddens=[256], dueling=True, layer_norm=False, **network_kwargs):
     if isinstance(network, str):
         from baselines.common.models import get_network_builder