import tensorflow as tf
import numpy as np


class ValueCNN:
    def __init__(self, scope, input_shape, is_trainable=True):
        with tf.variable_scope(scope):
            self.scope = scope
            self.states_ph = tf.placeholder(dtype=tf.float32, 
                                        shape=[None, *input_shape], ###################### 
                                        name="state")
            
            self.states_scaled = self.states_ph / 255.
            
            self.conv1 = tf.layers.conv2d(inputs=self.states_scaled, 
                                           filters=32, 
                                           kernel_size=[8, 8], 
                                           strides=4, 
                                           padding="valid", 
                                           activation=tf.nn.relu,
                                           trainable=is_trainable,
                                           name="conv1")
            
            self.conv2 = tf.layers.conv2d(inputs=self.conv1, 
                                           filters=64, 
                                           kernel_size=[4, 4], 
                                           strides=2, 
                                           padding="valid", 
                                           activation=tf.nn.relu, 
                                           trainable=is_trainable,
                                           name="conv2")
            
            self.conv3 = tf.layers.conv2d(inputs=self.conv2, 
                                           filters=64, 
                                           kernel_size=[3, 3], 
                                           strides=1, 
                                           padding="valid", 
                                           activation=tf.nn.relu, 
                                           trainable=is_trainable,
                                           name="conv3")
            
            self.flatten = tf.layers.flatten(inputs=self.conv3,
                                             name="flatten")
            
            self.dense1 = tf.layers.dense(inputs=self.flatten,
                                          units=512,
                                          activation=tf.nn.relu,
                                          trainable=is_trainable,
                                          name="dense1")
            
            self.values = tf.layers.dense(inputs=self.dense1, 
                                          units=1, 
                                          trainable=is_trainable,
                                          name="value")

            self.values = tf.squeeze(self.values)
            
            
            
            self.params = tf.get_collection(key=tf.GraphKeys.GLOBAL_VARIABLES, 
                                            scope=scope)
            
    def predict_batch_values(self, sess, states):
        return sess.run(self.values, {self.states_ph: states})
    

73c112a2cfe6dbd5087e4a62b9f76426271c9014
<mask token>

<mask token>


class ValueCNN:
    <mask token>

    def predict_batch_values(self, sess, states):
        return sess.run(self.values, {self.states_ph: states})

<mask token>


class ValueCNN:

    def __init__(self, scope, input_shape, is_trainable=True):
        with tf.variable_scope(scope):
            self.scope = scope
            self.states_ph = tf.placeholder(dtype=tf.float32, shape=[None,
                *input_shape], name='state')
            self.states_scaled = self.states_ph / 255.0
            self.conv1 = tf.layers.conv2d(inputs=self.states_scaled,
                filters=32, kernel_size=[8, 8], strides=4, padding='valid',
                activation=tf.nn.relu, trainable=is_trainable, name='conv1')
            self.conv2 = tf.layers.conv2d(inputs=self.conv1, filters=64,
                kernel_size=[4, 4], strides=2, padding='valid', activation=
                tf.nn.relu, trainable=is_trainable, name='conv2')
            self.conv3 = tf.layers.conv2d(inputs=self.conv2, filters=64,
                kernel_size=[3, 3], strides=1, padding='valid', activation=
                tf.nn.relu, trainable=is_trainable, name='conv3')
            self.flatten = tf.layers.flatten(inputs=self.conv3, name='flatten')
            self.dense1 = tf.layers.dense(inputs=self.flatten, units=512,
                activation=tf.nn.relu, trainable=is_trainable, name='dense1')
            self.values = tf.layers.dense(inputs=self.dense1, units=1,
                trainable=is_trainable, name='value')
            self.values = tf.squeeze(self.values)
            self.params = tf.get_collection(key=tf.GraphKeys.
                GLOBAL_VARIABLES, scope=scope)

    def predict_batch_values(self, sess, states):
        return sess.run(self.values, {self.states_ph: states})

import tensorflow as tf
import numpy as np


class ValueCNN:

    def __init__(self, scope, input_shape, is_trainable=True):
        with tf.variable_scope(scope):
            self.scope = scope
            self.states_ph = tf.placeholder(dtype=tf.float32, shape=[None,
                *input_shape], name='state')
            self.states_scaled = self.states_ph / 255.0
            self.conv1 = tf.layers.conv2d(inputs=self.states_scaled,
                filters=32, kernel_size=[8, 8], strides=4, padding='valid',
                activation=tf.nn.relu, trainable=is_trainable, name='conv1')
            self.conv2 = tf.layers.conv2d(inputs=self.conv1, filters=64,
                kernel_size=[4, 4], strides=2, padding='valid', activation=
                tf.nn.relu, trainable=is_trainable, name='conv2')
            self.conv3 = tf.layers.conv2d(inputs=self.conv2, filters=64,
                kernel_size=[3, 3], strides=1, padding='valid', activation=
                tf.nn.relu, trainable=is_trainable, name='conv3')
            self.flatten = tf.layers.flatten(inputs=self.conv3, name='flatten')
            self.dense1 = tf.layers.dense(inputs=self.flatten, units=512,
                activation=tf.nn.relu, trainable=is_trainable, name='dense1')
            self.values = tf.layers.dense(inputs=self.dense1, units=1,
                trainable=is_trainable, name='value')
            self.values = tf.squeeze(self.values)
            self.params = tf.get_collection(key=tf.GraphKeys.
                GLOBAL_VARIABLES, scope=scope)

    def predict_batch_values(self, sess, states):
        return sess.run(self.values, {self.states_ph: states})

import tensorflow as tf
import numpy as np


class ValueCNN:
    def __init__(self, scope, input_shape, is_trainable=True):
        with tf.variable_scope(scope):
            self.scope = scope
            self.states_ph = tf.placeholder(dtype=tf.float32, 
                                        shape=[None, *input_shape], ###################### 
                                        name="state")
            
            self.states_scaled = self.states_ph / 255.
            
            self.conv1 = tf.layers.conv2d(inputs=self.states_scaled, 
                                           filters=32, 
                                           kernel_size=[8, 8], 
                                           strides=4, 
                                           padding="valid", 
                                           activation=tf.nn.relu,
                                           trainable=is_trainable,
                                           name="conv1")
            
            self.conv2 = tf.layers.conv2d(inputs=self.conv1, 
                                           filters=64, 
                                           kernel_size=[4, 4], 
                                           strides=2, 
                                           padding="valid", 
                                           activation=tf.nn.relu, 
                                           trainable=is_trainable,
                                           name="conv2")
            
            self.conv3 = tf.layers.conv2d(inputs=self.conv2, 
                                           filters=64, 
                                           kernel_size=[3, 3], 
                                           strides=1, 
                                           padding="valid", 
                                           activation=tf.nn.relu, 
                                           trainable=is_trainable,
                                           name="conv3")
            
            self.flatten = tf.layers.flatten(inputs=self.conv3,
                                             name="flatten")
            
            self.dense1 = tf.layers.dense(inputs=self.flatten,
                                          units=512,
                                          activation=tf.nn.relu,
                                          trainable=is_trainable,
                                          name="dense1")
            
            self.values = tf.layers.dense(inputs=self.dense1, 
                                          units=1, 
                                          trainable=is_trainable,
                                          name="value")

            self.values = tf.squeeze(self.values)
            
            
            
            self.params = tf.get_collection(key=tf.GraphKeys.GLOBAL_VARIABLES, 
                                            scope=scope)
            
    def predict_batch_values(self, sess, states):
        return sess.run(self.values, {self.states_ph: states})
    
