Tensorflow Loss and accuracy error Neural Network - python-3.x

Question

Hope you are in good health. Actually I am facing a problem in my self built code. I am a beginner in Machine Learning and working on neural network. I build my own neural network and trying to train dataset with the help of tensorflow but getting alot of loss and having some problem with printing my accuracy Please view the code Thank you!
Here is my code I implement it on kaggle:
# This Python 3 environment comes with many helpful analytics libraries installed
# It is defined by the kaggle/python docker image: https://github.com/kaggle/docker-python
# For example, here's several helpful packages to load in
import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
from subprocess import check_output
from tensorflow.examples.tutorials.mnist import input_data
import tensorflow as tf
# Input data files are available in the "../input/" directory.
# For example, running this (by clicking run or pressing Shift+Enter) will list the files in the input directory
import os
print(os.listdir("../input"))
print(check_output(["ls", "../input"]).decode("utf8"))
mnist = input_data.read_data_sets('../input',one_hot=True)
x_train=mnist.train.images
y_train=mnist.train.labels
x_test=mnist.test.images
y_test=mnist.test.labels
# Any results you write to the current directory are saved as output.
layer1_neuron=500
layer2_neuron=500
layer3_neuron=500
number_of_class=10
batch_size=200
x=tf.placeholder('float',[None,784]) #28 * 28 is 784 (shape of the data)
y=tf.placeholder('float')
#my neural network
def neural_network(x_train):
hidden_layer_1={
'weights':tf.Variable(tf.random_normal([784,layer1_neuron])),
'biases': tf.Variable(tf.random_normal([layer1_neuron]))
}
hidden_layer_2={
'weights':tf.Variable(tf.random_normal([layer1_neuron,layer2_neuron])),
'biases':tf.Variable(tf.random_normal([layer2_neuron]))
}
hidden_layer_3={
'weights':tf.Variable(tf.random_normal([layer2_neuron,layer3_neuron])),
'biases':tf.Variable(tf.random_normal([layer3_neuron]))
}
output={
'weights':tf.Variable(tf.random_normal([layer3_neuron,number_of_class])),
'biases':tf.Variable(tf.random_normal([number_of_class]))
}
l1=tf.add(tf.matmul(x_train,hidden_layer_1['weights']),hidden_layer_1['biases'])
l1=tf.nn.relu(l1)
l2=tf.add(tf.matmul(l1,hidden_layer_2['weights']),hidden_layer_2['biases'])
l2=tf.nn.relu(l2)
l3=tf.add(tf.matmul(l2,hidden_layer_3['weights']),hidden_layer_3['biases'])
l3=tf.nn.relu(l3)
output=tf.add(tf.matmul(l3,output['weights']),output['biases'])
return output
# for splitting out batches of data
def next_batch(num, data, labels):
idx = np.arange(0 , len(data))
np.random.shuffle(idx)
idx = idx[:num]
data_shuffle = [data[ i] for i in idx]
labels_shuffle = [labels[ i] for i in idx]
return np.asarray(data_shuffle), np.asarray(labels_shuffle)
def traning_neuralNetwork(x_test,x_train,y_test,y_train):
total_epochs=5
total_loss=0
epoch_loss=0
batch_size=200
num_batch = int(np.ceil(42000/batch_size))
prediction=[]
prediction=neural_network(x_train)
cost=tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=prediction,labels=y_train))
optimizer=tf.train.AdamOptimizer().minimize(cost)
with tf.Session() as sess:
sess.run(tf.global_variables_initializer())
for epoch in range (total_epochs):
epoch_loss=0
for _ in range (num_batch):
x_train,y_train=next_batch(batch_size,x_train,y_train)
_,epoch_loss=sess.run([optimizer,cost],feed_dict={x:x_train,y:y_train})
total_loss+=epoch_loss
print('Epoch ',epoch, " loss = ",total_loss)
print("Traning Complete!")
correct=tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
accuracy=tf.reduce_mean(tf.cast(correct,'float'))
print('accuracy',accuracy.eval({x : x_test,y : y_test}))
traning_neuralNetwork(x_test,x_train,y_test,y_train)
Output Error
WARNING:tensorflow:From <ipython-input-3-92b45e11aa74>:61: softmax_cross_entropy_with_logits (from tensorflow.python.ops.nn_ops) is deprecated and will be removed in a future version.
Instructions for updating:
Future major versions of TensorFlow will allow gradients to flow
into the labels input on backprop by default.
See tf.nn.softmax_cross_entropy_with_logits_v2.
Epoch 0 loss = 968685.6919555664
Epoch 1 loss = 1076421.9005126953
Epoch 2 loss = 1108946.4575500488
Epoch 3 loss = 1117600.8527259827
Epoch 4 loss = 1119452.7342455387
Traning Complete!
---------------------------------------------------------------------------
InvalidArgumentError Traceback (most recent call last)
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1329 try:
-> 1330 return fn(*args)
1331 except errors.OpError as e:
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run_fn(feed_dict, fetch_list, target_list, options, run_metadata)
1314 return self._call_tf_sessionrun(
-> 1315 options, feed_dict, fetch_list, target_list, run_metadata)
1316
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _call_tf_sessionrun(self, options, feed_dict, fetch_list, target_list, run_metadata)
1422 self._session, options, feed_dict, fetch_list, target_list,
-> 1423 status, run_metadata)
1424
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/errors_impl.py in __exit__(self, type_arg, value_arg, traceback_arg)
515 compat.as_text(c_api.TF_Message(self.status.status)),
--> 516 c_api.TF_GetCode(self.status.status))
517 # Delete the underlying status object from memory otherwise it stays alive
InvalidArgumentError: Incompatible shapes: [55000] vs. [10000]
[[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/device:GPU:0"](ArgMax, ArgMax_1)]]
During handling of the above exception, another exception occurred:
InvalidArgumentError Traceback (most recent call last)
<ipython-input-4-6ce6d479d3df> in <module>()
1
2
----> 3 traning_neuralNetwork(x_test,x_train,y_test,y_train)
<ipython-input-3-92b45e11aa74> in traning_neuralNetwork(x_test, x_train, y_test, y_train)
75 correct=tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
76 accuracy=tf.reduce_mean(tf.cast(correct,'float'))
---> 77 print('accuracy',accuracy.eval({x : x_test,y : y_test}))
78
79
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in eval(self, feed_dict, session)
659
660 """
--> 661 return _eval_using_default_session(self, feed_dict, self.graph, session)
662
663
/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py in _eval_using_default_session(tensors, feed_dict, graph, session)
5061 "the tensor's graph is different from the session's "
5062 "graph.")
-> 5063 return session.run(tensors, feed_dict)
5064
5065
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in run(self, fetches, feed_dict, options, run_metadata)
906 try:
907 result = self._run(None, fetches, feed_dict, options_ptr,
--> 908 run_metadata_ptr)
909 if run_metadata:
910 proto_data = tf_session.TF_GetBuffer(run_metadata_ptr)
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _run(self, handle, fetches, feed_dict, options, run_metadata)
1141 if final_fetches or final_targets or (handle and feed_dict_tensor):
1142 results = self._do_run(handle, final_targets, final_fetches,
-> 1143 feed_dict_tensor, options, run_metadata)
1144 else:
1145 results = []
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_run(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)
1322 if handle is None:
1323 return self._do_call(_run_fn, feeds, fetches, targets, options,
-> 1324 run_metadata)
1325 else:
1326 return self._do_call(_prun_fn, handle, feeds, fetches)
/opt/conda/lib/python3.6/site-packages/tensorflow/python/client/session.py in _do_call(self, fn, *args)
1341 except KeyError:
1342 pass
-> 1343 raise type(e)(node_def, op, message)
1344
1345 def _extend_graph(self):
InvalidArgumentError: Incompatible shapes: [55000] vs. [10000]
[[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/device:GPU:0"](ArgMax, ArgMax_1)]]
Caused by op 'Equal', defined at:
File "/opt/conda/lib/python3.6/runpy.py", line 193, in _run_module_as_main
"__main__", mod_spec)
File "/opt/conda/lib/python3.6/runpy.py", line 85, in _run_code
exec(code, run_globals)
File "/opt/conda/lib/python3.6/site-packages/ipykernel_launcher.py", line 16, in <module>
app.launch_new_instance()
File "/opt/conda/lib/python3.6/site-packages/traitlets/config/application.py", line 658, in launch_instance
app.start()
File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelapp.py", line 477, in start
ioloop.IOLoop.instance().start()
File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/ioloop.py", line 177, in start
super(ZMQIOLoop, self).start()
File "/opt/conda/lib/python3.6/site-packages/tornado/ioloop.py", line 888, in start
handler_func(fd_obj, events)
File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 440, in _handle_events
self._handle_recv()
File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 472, in _handle_recv
self._run_callback(callback, msg)
File "/opt/conda/lib/python3.6/site-packages/zmq/eventloop/zmqstream.py", line 414, in _run_callback
callback(*args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/tornado/stack_context.py", line 277, in null_wrapper
return fn(*args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 283, in dispatcher
return self.dispatch_shell(stream, msg)
File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 235, in dispatch_shell
handler(stream, idents, msg)
File "/opt/conda/lib/python3.6/site-packages/ipykernel/kernelbase.py", line 399, in execute_request
user_expressions, allow_stdin)
File "/opt/conda/lib/python3.6/site-packages/ipykernel/ipkernel.py", line 196, in do_execute
res = shell.run_cell(code, store_history=store_history, silent=silent)
File "/opt/conda/lib/python3.6/site-packages/ipykernel/zmqshell.py", line 533, in run_cell
return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)
File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2698, in run_cell
interactivity=interactivity, compiler=compiler, result=result)
File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2808, in run_ast_nodes
if self.run_code(code, result):
File "/opt/conda/lib/python3.6/site-packages/IPython/core/interactiveshell.py", line 2862, in run_code
exec(code_obj, self.user_global_ns, self.user_ns)
File "<ipython-input-4-6ce6d479d3df>", line 3, in <module>
traning_neuralNetwork(x_test,x_train,y_test,y_train)
File "<ipython-input-3-92b45e11aa74>", line 75, in traning_neuralNetwork
correct=tf.equal(tf.argmax(prediction,1),tf.argmax(y,1))
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/ops/gen_math_ops.py", line 2455, in equal
"Equal", x=x, y=y, name=name)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 3306, in create_op
op_def=op_def)
File "/opt/conda/lib/python3.6/site-packages/tensorflow/python/framework/ops.py", line 1669, in __init__
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access
InvalidArgumentError (see above for traceback): Incompatible shapes: [55000] vs. [10000]
[[Node: Equal = Equal[T=DT_INT64, _device="/job:localhost/replica:0/task:0/device:GPU:0"](ArgMax, ArgMax_1)]]

Answer 1

Your data Matrix is a 4D array of size (48000 * 28 * 28 * 1) which is > 784,500.
As a side note, 32,928,000 is actually 28 * 28 * 42000, probably X_train matrix has a different shape.
Could you add print(X_train.shape[0]) and double check your matrix?
To solve your problem with GraphDef, have a look at this response:
https://stackoverflow.com/a/36358913/4601719
tf.initialize_all_variables is depreciated, please use tf.global_variables_initializer as recommended here:
https://www.tensorflow.org/api_docs/python/tf/initialize_all_variables