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# Copyright 2015 Google Inc. All Rights Reserved.
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#     http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,

awjuliani

awjuliani

import os
newpath = r'./csvs' 
if not os.path.exists(newpath):
    os.makedirs(newpath)

iters = 0
for i in finalRepresentations:
    tsne = TSNE(perplexity=50, n_components=3, init='pca', n_iter=5000)
    plot_only = 2000
    lowDWeights = tsne.fit_transform(i[0:plot_only,:])

awjuliani

awjuliani

class AC_Network():
    def __init__(self,s_size,a_size,scope,trainer):
        with tf.variable_scope(scope):
            #Input and visual encoding layers
            self.inputs = tf.placeholder(shape=[None,s_size],dtype=tf.float32)
            self.imageIn = tf.reshape(self.inputs,shape=[-1,84,84,1])
            self.conv1 = slim.conv2d(activation_fn=tf.nn.elu,
                inputs=self.imageIn,num_outputs=16,
                kernel_size=[8,8],stride=[4,4],padding='VALID')
            self.conv2 = slim.conv2d(activation_fn=tf.nn.elu,

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awjuliani