From d90dd2e1e195b87e96266c61829c7b78f0539cda Mon Sep 17 00:00:00 2001
From: Sai Raghava Reddy Ganapaa <sai-raghava-reddy.ganapa@hsrw.org>
Date: Tue, 3 Sep 2019 18:10:25 +0200
Subject: [PATCH] fixing preconditon errors with weights

---
 .../knowledge_transfer.cpython-36.pyc         | Bin 6205 -> 5144 bytes
 .../__pycache__/leg_dynamics.cpython-36.pyc   | Bin 13959 -> 13959 bytes
 .../__pycache__/my_network.cpython-36.pyc     | Bin 4665 -> 4665 bytes
 spider_control/control1.py                    |  10 +-
 spider_control/knowledge_transfer.py          |  98 +++++++++++-------
 5 files changed, 67 insertions(+), 41 deletions(-)

diff --git a/spider_control/__pycache__/knowledge_transfer.cpython-36.pyc b/spider_control/__pycache__/knowledge_transfer.cpython-36.pyc
index 4a1553cc388fa8b34a2971295172a0a9547783df..e768297ea7f4e599855ac1349064733ffde88fc4 100644
GIT binary patch
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diff --git a/spider_control/__pycache__/leg_dynamics.cpython-36.pyc b/spider_control/__pycache__/leg_dynamics.cpython-36.pyc
index e3acb690c50ce73879f6e6c7fcd3c35ad26aca41..ad33e58cdb0f4b55e3658926f86a7f47fa4f48de 100644
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diff --git a/spider_control/__pycache__/my_network.cpython-36.pyc b/spider_control/__pycache__/my_network.cpython-36.pyc
index 1da9b53fd8d9cb90cccee872aab7df580ff333ac..eed87b38b4403fe9c137f7359e9ed47455bf8153 100644
GIT binary patch
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diff --git a/spider_control/control1.py b/spider_control/control1.py
index fda6c34..b2d6575 100755
--- a/spider_control/control1.py
+++ b/spider_control/control1.py
@@ -45,8 +45,8 @@ def joint_callback(data, args):
     simulation_mode = args[0] #to distinguish between training and testing
     model_number = args[1]#to distiguish between the type of model to train incase of training 
     ou = tf.one_hot([model_number-1], 4)
-    with tf.Session() as session:
-        out = session.run(ou)
+    #with tf.Session() as session:
+    #    out = session.run(ou)
     #out = kt.one_hot_encoding(model_number)
     rospy.loginfo(data.position)#testing
     pub_msg = JointState() # Make a new msg to publish results
@@ -60,7 +60,7 @@ def joint_callback(data, args):
     carollis_inp = leg.carollis_input()
     print("carollis input is ")#testing
     print(carollis_inp)
-    knowledge_out = knowledge.run(carollis_inp)
+    knowledge_out, out_tensor = knowledge.run(carollis_inp)
     model_num = np.where(knowledge_out == np.amax(knowledge_out))
     reward = leg.leg_run()
     if(model_num == 0):
@@ -80,7 +80,7 @@ def joint_callback(data, args):
         pub_msg.position = new_position
         model4.nn_learn(reward)
     if(simulation_mode == 'train'):
-        knowledge.learn(out, knowledge_out)
+        knowledge.learn(out_tensor, ou)
     leg.update_angles(new_position)
     leg.update_effort(effort)
     leg.update_velocity(velocity)
@@ -98,7 +98,7 @@ if __name__=='__main__':
     eff = 100
     tact = np.zeros(8)
     leg = ld.Leg_attribute(pos, vel, eff, tact)
-    mode=input('please enter whether you want to test or train ?')
+    mode = input('please enter whether you want to test or train ?')
     if(mode == 'train'):
         model_number = int(input('please enter the model you wish to train i.e. 1, 2, 3, 4 \n '))
     rospy.init_node('joint_logger_node', anonymous = True)
diff --git a/spider_control/knowledge_transfer.py b/spider_control/knowledge_transfer.py
index 0f1b10c..8d4271e 100755
--- a/spider_control/knowledge_transfer.py
+++ b/spider_control/knowledge_transfer.py
@@ -62,35 +62,35 @@ class MultiClassLogistic:
         for i in range(len(inputs)):
             inputs[i] = (inputs[i]-a_min)/(a_max-a_min)
     
-    def input_weight(self):
-        with tf.compat.v1.variable_scope("weight_in", reuse=tf.compat.v1.AUTO_REUSE):
-            v = tf.compat.v1.get_variable("weight_input", dtype=tf.float64, shape=[self.neurons, 1], initializer=self.weight_initer)
-        return v
+    #def input_weight(self):
+        #with tf.compat.v1.variable_scope("weight_in", reuse=tf.compat.v1.AUTO_REUSE):
+            #v = tf.compat.v1.get_variable("weight_input", dtype=tf.float64, shape=[self.neurons, 1], initializer=self.weight_initer)
+        #return v
 
-    def hid_weight(self):
-        with tf.compat.v1.variable_scope("weight_hid", reuse=tf.compat.v1.AUTO_REUSE):
-            v = tf.compat.v1.get_variable("weight_hidden", dtype=tf.float64, shape=[self.neurons, 1], initializer=self.weight_initer1)
-        return v
+    #def hid_weight(self):
+        #with tf.compat.v1.variable_scope("weight_hid", reuse=tf.compat.v1.AUTO_REUSE):
+            #v = tf.compat.v1.get_variable("weight_hidden", dtype=tf.float64, shape=[self.neurons, 1], initializer=self.weight_initer1)
+        #return v
 
-    def out_weight(self):
-        with tf.compat.v1.variable_scope("weight_out", reuse=tf.compat.v1.AUTO_REUSE):
-            v = tf.compat.v1.get_variable("weight_input", dtype=tf.float64, shape=[4, 2], initializer=self.weight_initer2)
-        return v
+    #def out_weight(self):
+        #with tf.compat.v1.variable_scope("weight_out", reuse=tf.compat.v1.AUTO_REUSE):
+            #v = tf.compat.v1.get_variable("weight_input", dtype=tf.float64, shape=[4, 2], initializer=self.weight_initer2)
+        #return v
 
-    def bias_in(self):
-        with tf.compat.v1.variable_scope("bias_in", reuse=tf.compat.v1.AUTO_REUSE):
-            v = tf.compat.v1.get_variable("bias_input", dtype=tf.float64, shape=[self.neurons, 1], initializer=self.bias_initer)
-        return v
+    #def bias_in(self):
+    #    with tf.compat.v1.variable_scope("bias_in", reuse=tf.compat.v1.AUTO_REUSE):
+    #        v = tf.compat.v1.get_variable("bias_input", dtype=tf.float64, shape=[self.neurons, 1], initializer=self.bias_initer)
+    #    return v
 
-    def bias_hid(self):
-        with tf.compat.v1.variable_scope("bias_hidd", reuse=tf.compat.v1.AUTO_REUSE):
-            v = tf.compat.v1.get_variable("bias_hidden", dtype=tf.float64, shape=[self.neurons, 1], initializer=self.bias_initer1)
-        return v
+    #def bias_hid(self):
+    #    with tf.compat.v1.variable_scope("bias_hidd", reuse=tf.compat.v1.AUTO_REUSE):
+    #        v = tf.compat.v1.get_variable("bias_hidden", dtype=tf.float64, shape=[self.neurons, 1], initializer=self.bias_initer1)
+    #    return v
 
-    def bias_out(self):
-        with tf.compat.v1.variable_scope("bias_outt", reuse=tf.compat.v1.AUTO_REUSE):
-            v = tf.compat.v1.get_variable("bias_out", dtype=tf.float64, shape=[4, 1], initializer=self.bias_initer2)
-        return v
+    #def bias_out(self):
+    #    with tf.compat.v1.variable_scope("bias_outt", reuse=tf.compat.v1.AUTO_REUSE):
+    #        v = tf.compat.v1.get_variable("bias_out", dtype=tf.float64, shape=[1, 4], initializer=self.bias_initer2)
+    #    return v
     def run(self, carollis_input):
         c_in=normalize(carollis_input, axis = 0)
         print('normalized carollis input is \n')
@@ -98,28 +98,53 @@ class MultiClassLogistic:
         c_in = np.array(c_in)
         c_input = tf.compat.v1.convert_to_tensor(c_in, tf.float64)
         #'finding the output of the input layer'
-        weight_i = self.input_weight()
-        weight_h = self.hid_weight()
-        weight_o = self.out_weight()
-        bias_i = self.bias_in()
-        bias_h = self.bias_hid()
-        bias_o = self.bias_out()
-        init = tf.global_variables_initializer()
+        with tf.compat.v1.variable_scope("weight_in", reuse=tf.compat.v1.AUTO_REUSE):
+            weight_i = tf.compat.v1.get_variable("weight_input", dtype=tf.float64, shape=[self.neurons, 1], initializer=self.weight_initer)
+        with tf.compat.v1.variable_scope("weight_hid", reuse=tf.compat.v1.AUTO_REUSE):   
+            weight_h = tf.compat.v1.get_variable("weight_hidden", dtype=tf.float64, shape=[self.neurons, 1], initializer=self.weight_initer1)
+        with tf.compat.v1.variable_scope("weight_out", reuse=tf.compat.v1.AUTO_REUSE): 
+            weight_o = tf.compat.v1.get_variable("weight_input", dtype=tf.float64, shape=[4, 2], initializer=self.weight_initer2)
+        with tf.compat.v1.variable_scope("bias_in", reuse=tf.compat.v1.AUTO_REUSE): 
+            bias_i = tf.compat.v1.get_variable("bias_input", dtype=tf.float64, shape=[self.neurons, 1], initializer=self.bias_initer)
+        with tf.compat.v1.variable_scope("bias_hid", reuse=tf.compat.v1.AUTO_REUSE): 
+            bias_h = tf.compat.v1.get_variable("bias_hidden", dtype=tf.float64, shape=[self.neurons, 1], initializer=self.bias_initer1)
+        with tf.compat.v1.variable_scope("bias_out", reuse=tf.compat.v1.AUTO_REUSE): 
+            bias_o = tf.compat.v1.get_variable("bias_out", dtype=tf.float64, shape=[1, 4], initializer=self.bias_initer2)
+        #weight_i = self.input_weight()
+        #weight_h = self.hid_weight()
+        #weight_o = self.out_weight()
+        #bias_i = self.bias_in()
+        #bias_h = self.bias_hid()
+        #bias_o = self.bias_out()
+        uninitialized_variables = []
         sess = tf.Session()
+        print("printing the names of all variables")
+        print(tf.all_variables())
+        for var in tf.all_variables():
+            try:
+                sess.run(var)
+            except tf.errors.FailedPreconditionError:
+                uninitialized_variables.append(var)
+        init = tf.variables_initializer(uninitialized_variables)
         sess.run(init)
         knowledge_input = tf.add(tf.multiply(c_input, weight_i), bias_i)
-        sess.run(knowledge_input)
+        #sess.run(knowledge_input)
         knowledge_hidden = tf.nn.leaky_relu(knowledge_input, alpha=0.01) 
         #'calculating the output of hidden layer'
         knowledge_hidden_output = 3.14*(tf.add(tf.multiply(knowledge_hidden, weight_h), bias_h))#input function of hidden layer
         knowledge_hidden_out = tf.nn.leaky_relu(knowledge_hidden_output, alpha=0.01, name='leaky_relu')
           
-        sess.run(knowledge_hidden_out)
+        #sess.run(knowledge_hidden_out)
         #'calculating the input of output layer'
         knowledge_hidden_out = tf.reshape(knowledge_hidden_out, [4, 2])#for quadrant method
         out_mult = tf.multiply(knowledge_hidden_out, weight_o)
         out_add = tf.add(out_mult[:, 0], out_mult[:, 1])
         in_out = tf.add(out_add, bias_o)
+        #deb = sess.run(in_out)
+        #print("printing the shape of tensor in last layer")
+        #print(tf.shape(in_out))
+        #print(deb)
+        in_out = tf.reshape(in_out, [1, 4])
         #i_o = sess.run(in_out)
         #r_i_o = np.reshape(i_o, (8, 1))
         #outt = np.add(r_i_o[0:4, 0], r_i_o[4:8, 0])
@@ -129,9 +154,10 @@ class MultiClassLogistic:
         output = sess.run(in_out)
         #output = sess.run(in_out)
         #'finding the softmax output of the neurons'
-        softmax_output = tf.nn.softmax(in_out)
-        output = sess.run(softmax_output)
-        return output
+        #just testing
+        #softmax_output = tf.nn.softmax(in_out)
+        #output = sess.run(softmax_output)
+        return output, in_out
     def learn(self, preferred_out, soft_out):
         self.kt_learning(0.1, soft_out, preferred_out)
     
-- 
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