nethsix/gentle_tensorflow

Gentle introduction to Tensorflow

gentle_tensorflow's Issues

suggest about learn_rate or steps

@nethsix Thanks for your tutorial :)

current

steps = 100
learn_rate = 0.0000001

I think it is better to adjust learn_rate or steps in the code because beginner who is not familiar with machine learning concept yet have question about value of W, b.

and what about adding the cost? I think it will show the how the cost is changed
ex) print("Cost: {0}".format(sess.run(cost, feed_dict=feed)))

linear_regression_one_feature_using_mini_batch_with_tensorboard.py is not valid

There is some issue with this file. Training doesnt provide intented results. I see the actual b value is 10.

"You must feed a value for placeholder tensor 'Placeholder_1' with dtype double and shape [?,1]"

Hi Khor,

Above all, thanks for your "linear_regression_one_feature.py" as I've finally understood what running linear regression in Tensorflow is after weeks of reading and tweaking.

I've tried to reproduce your code and simply exchange X and Y with data respectively on how often students go out with friends (from 1 to 5) and on profession of the father (from 0 to 4).

Here is the code:

import numpy as np
import tensorflow as tf
import csv

x = [3.0, 2.0, 2.0, 2.0, 2.0, 4.0, 4.0, 2.0, 1.0, 3.0, 2.0, 3.0, 3.0, 2.0, 4.0, 3.0, 2.0, 5.0, 3.0, 1.0, 2.0, 1.0, 4.0, 2.0, 2.0, 2.0, 4.0, 3.0, 5.0, 2.0, 1.0, 2.0, 2.0, 3.0, 1.0, 3.0, 3.0, 2.0, 1.0, 3.0, 3.0, 3.0, 1.0, 3.0, 2.0, 5.0, 2.0, 3.0, 4.0, 3.0, 3.0, 5.0, 4.0, 4.0, 4.0, 2.0, 2.0, 2.0, 3.0, 4.0, 5.0, 3.0, 4.0, 4.0, 3.0, 3.0, 3.0, 3.0, 2.0, 2.0, 3.0, 4.0, 2.0, 3.0, 3.0, 3.0, 3.0, 1.0, 3.0, 2.0, 2.0, 4.0, 3.0, 2.0, 4.0, 4.0, 3.0, 2.0, 3.0, 3.0, 5.0, 3.0, 3.0, 4.0, 2.0, 3.0, 5.0, 4.0, 5.0, 5.0, 3.0, 3.0, 5.0, 4.0, 3.0, 2.0, 3.0, 5.0, 5.0, 3.0, 2.0, 2.0, 2.0, 2.0, 4.0, 3.0, 2.0, 4.0, 3.0, 3.0, 4.0, 2.0, 4.0, 4.0, 5.0, 2.0, 2.0, 3.0, 5.0, 2.0, 3.0, 4.0, 1.0, 3.0, 3.0, 5.0, 2.0, 4.0, 2.0, 2.0, 3.0, 2.0, 4.0, 5.0, 2.0, 2.0, 2.0, 2.0, 5.0, 5.0, 4.0, 1.0, 4.0, 1.0, 4.0, 2.0, 5.0, 2.0, 5.0, 2.0, 4.0, 4.0, 3.0, 5.0, 2.0, 5.0, 3.0, 5.0, 2.0, 5.0, 2.0, 4.0, 5.0, 5.0, 4.0, 4.0, 4.0, 3.0, 3.0, 3.0, 3.0, 2.0, 3.0, 2.0, 4.0, 3.0, 3.0, 3.0, 3.0, 3.0, 3.0, 4.0, 2.0, 3.0, 2.0, 3.0, 4.0, 4.0, 2.0, 5.0, 3.0, 4.0, 2.0, 5.0, 3.0, 3.0, 2.0, 2.0, 2.0, 3.0, 5.0, 4.0, 4.0, 1.0, 4.0, 5.0, 4.0, 3.0, 3.0, 5.0, 4.0, 1.0, 3.0, 2.0, 3.0, 4.0, 3.0, 2.0, 3.0, 3.0, 2.0, 5.0, 4.0, 2.0, 3.0, 2.0, 2.0, 1.0, 4.0, 5.0, 3.0, 5.0, 2.0, 4.0, 3.0, 2.0, 5.0, 3.0, 2.0, 5.0, 3.0, 5.0, 2.0, 2.0, 4.0, 3.0, 1.0, 4.0, 1.0, 2.0, 2.0, 2.0, 3.0, 3.0, 5.0, 4.0, 4.0, 5.0, 5.0, 5.0, 5.0, 3.0, 2.0, 3.0, 4.0, 1.0, 4.0, 4.0, 3.0, 4.0, 4.0, 2.0, 4.0, 5.0, 3.0, 3.0, 3.0, 4.0, 3.0, 2.0, 2.0, 3.0, 2.0, 2.0, 3.0, 4.0, 5.0, 3.0, 2.0, 4.0, 1.0, 3.0, 2.0, 4.0, 3.0, 3.0, 4.0, 3.0, 3.0, 4.0, 1.0, 2.0, 2.0, 2.0, 2.0, 3.0, 4.0, 4.0, 4.0, 2.0, 2.0, 2.0, 3.0, 3.0, 3.0, 5.0, 5.0, 4.0, 3.0, 5.0, 3.0, 4.0, 3.0, 4.0, 5.0, 3.0, 2.0, 3.0, 3.0, 4.0, 3.0, 3.0, 1.0, 3.0, 3.0, 2.0, 5.0, 3.0, 5.0, 4.0, 4.0, 3.0, 4.0, 5.0, 4.0, 3.0, 3.0, 2.0, 2.0, 4.0, 3.0, 2.0, 4.0, 2.0, 4.0, 2.0, 1.0, 3.0, 2.0, 2.0, 3.0, 3.0, 5.0, 4.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 3.0, 3.0, 2.0, 3.0, 3.0, 3.0, 2.0, 4.0, 1.0, 4.0, 5.0, 3.0, 1.0, 3.0]

y = [1.0, 1.0, 4.0, 3.0, 4.0, 2.0, 4.0, 3.0, 3.0, 4.0, 2.0, 4.0, 4.0, 2.0, 4.0, 4.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 2.0, 2.0, 2.0, 2.0, 4.0, 3.0, 4.0, 4.0, 4.0, 4.0, 3.0, 3.0, 2.0, 4.0, 4.0, 3.0, 2.0, 2.0, 4.0, 4.0, 2.0, 2.0, 4.0, 3.0, 4.0, 4.0, 4.0, 2.0, 4.0, 4.0, 4.0, 3.0, 2.0, 4.0, 4.0, 1.0, 4.0, 4.0, 1.0, 1.0, 4.0, 4.0, 4.0, 4.0, 3.0, 2.0, 3.0, 3.0, 4.0, 1.0, 3.0, 3.0, 4.0, 4.0, 2.0, 2.0, 3.0, 2.0, 2.0, 3.0, 2.0, 1.0, 4.0, 2.0, 4.0, 2.0, 4.0, 3.0, 4.0, 3.0, 4.0, 2.0, 1.0, 4.0, 2.0, 4.0, 4.0, 4.0, 4.0, 4.0, 3.0, 3.0, 3.0, 2.0, 3.0, 4.0, 4.0, 4.0, 3.0, 2.0, 4.0, 2.0, 4.0, 4.0, 3.0, 1.0, 3.0, 1.0, 2.0, 2.0, 4.0, 2.0, 3.0, 3.0, 0.0, 2.0, 4.0, 3.0, 1.0, 2.0, 3.0, 3.0, 4.0, 3.0, 3.0, 1.0, 4.0, 4.0, 2.0, 4.0, 1.0, 2.0, 1.0, 3.0, 1.0, 4.0, 2.0, 1.0, 2.0, 3.0, 3.0, 4.0, 2.0, 1.0, 1.0, 2.0, 3.0, 2.0, 3.0, 1.0, 1.0, 1.0, 3.0, 2.0, 4.0, 2.0, 4.0, 3.0, 1.0, 4.0, 1.0, 3.0, 4.0, 2.0, 3.0, 4.0, 4.0, 4.0, 3.0, 2.0, 3.0, 3.0, 3.0, 1.0, 2.0, 3.0, 1.0, 2.0, 1.0, 1.0, 3.0, 2.0, 2.0, 4.0, 3.0, 4.0, 4.0, 4.0, 2.0, 1.0, 2.0, 2.0, 3.0, 3.0, 4.0, 1.0, 4.0, 3.0, 4.0, 2.0, 2.0, 4.0, 3.0, 4.0, 3.0, 2.0, 2.0, 2.0, 1.0, 2.0, 2.0, 4.0, 3.0, 3.0, 2.0, 2.0, 2.0, 4.0, 2.0, 4.0, 4.0, 1.0, 3.0, 2.0, 2.0, 2.0, 2.0, 4.0, 4.0, 4.0, 4.0, 2.0, 2.0, 2.0, 3.0, 3.0, 0.0, 3.0, 3.0, 2.0, 2.0, 2.0, 1.0, 4.0, 4.0, 2.0, 2.0, 4.0, 4.0, 3.0, 3.0, 2.0, 3.0, 3.0, 4.0, 4.0, 2.0, 3.0, 2.0, 1.0, 1.0, 2.0, 2.0, 3.0, 4.0, 4.0, 4.0, 4.0, 3.0, 1.0, 1.0, 2.0, 1.0, 2.0, 1.0, 2.0, 4.0, 4.0, 4.0, 2.0, 3.0, 3.0, 3.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 4.0, 3.0, 3.0, 2.0, 3.0, 4.0, 3.0, 1.0, 1.0, 2.0, 1.0, 3.0, 1.0, 2.0, 2.0, 4.0, 3.0, 4.0, 4.0, 2.0, 2.0, 3.0, 0.0, 4.0, 3.0, 2.0, 4.0, 4.0, 2.0, 2.0, 3.0, 2.0, 2.0, 3.0, 3.0, 3.0, 3.0, 3.0, 2.0, 4.0, 3.0, 2.0, 2.0, 3.0, 4.0, 4.0, 4.0, 3.0, 1.0, 3.0, 1.0, 1.0, 4.0, 3.0, 4.0, 3.0, 1.0, 1.0, 1.0, 1.0, 3.0, 4.0, 1.0, 1.0, 4.0, 1.0, 2.0, 4.0, 3.0, 1.0, 2.0, 1.0, 4.0, 1.0, 4.0, 4.0, 3.0, 3.0, 4.0, 2.0, 2.0, 1.0, 4.0, 2.0, 4.0, 2.0, 3.0, 1.0, 2.0, 3.0, 1.0, 3.0, 1.0]

train_x = np.array(x) #printing train_x.dtype gives "float64"
train_x.shape = (394,1) # reshaping to be sure it fits the placeholder

train_y = np.array(y) #printing train_y.dtype gives "float64"
train_y.shape = (394,1) # reshaping to be sure it fits the placeholder

x = tf.placeholder(tf.float64,shape=(None,1)) #specifying placeholder dtype to fit my arrray's one
W = tf.Variable(tf.zeros([1,1],dtype=tf.float64)) #specifying dtype to fit the training set
b = tf.Variable(tf.zeros([1],dtype=tf.float64))
product = tf.matmul(x,W)
y = product + b
y_ = tf.placeholder(tf.float64,shape=(None,1)) #specifying placeholder dtype to fit my arrray's one

cost = tf.reduce_mean(tf.square(y_-y))

gradient = tf.train.GradientDescentOptimizer(0.001).minimize(cost)

with tf.Session() as sess:
init = tf.global_variables_initializer()
sess.run(init)

epochs = 1000
for i in range(epochs):
    feed = {x:train_x,y:train_y}
    sess.run(gradient,feed_dict=feed)
    if epoch % 50 == 0:
        print("After {} iteration".format(i),"W= {}".format(sess.run(W),\
                                                            "b= {}".format(sess.run(b))))
    print("Cost= {}".format(sess.run(cost,feed_dict=feed)))

---

The Traceback highlights "You must feed a value for placeholder tensor 'Placeholder_1' with dtype double and shape [?,1]" while placeholders' dtype and shape fit with those of the array. Here the full message:

2017-11-10 18:58:41.632505: I C:\tf_jenkins\home\workspace\rel-win\M\windows\PY\36\tensorflow\core\platform\cpu_feature_guard.cc:137] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
Traceback (most recent call last):
File "C:\python_for_tensorflow\tensorflow_env\lib\site-packages\tensorflow\python\client\session.py", line 1323, in _do_call
return fn(*args)
File "C:\python_for_tensorflow\tensorflow_env\lib\site-packages\tensorflow\python\client\session.py", line 1302, in _run_fn
status, run_metadata)
File "C:\python_for_tensorflow\tensorflow_env\lib\site-packages\tensorflow\python\framework\errors_impl.py", line 473, in exit
c_api.TF_GetCode(self.status.status))
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_1' with dtype double and shape [?,1]
[[Node: Placeholder_1 = Placeholderdtype=DT_DOUBLE, shape=[?,1], _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
File "C:\Users\Grégory\eclipse-workspace\tensorflow_project\Udacity_1.py", line 42, in
sess.run(gradient,feed_dict=feed)
File "C:\python_for_tensorflow\tensorflow_env\lib\site-packages\tensorflow\python\client\session.py", line 889, in run
run_metadata_ptr)
File "C:\python_for_tensorflow\tensorflow_env\lib\site-packages\tensorflow\python\client\session.py", line 1120, in _run
feed_dict_tensor, options, run_metadata)
File "C:\python_for_tensorflow\tensorflow_env\lib\site-packages\tensorflow\python\client\session.py", line 1317, in _do_run
options, run_metadata)
File "C:\python_for_tensorflow\tensorflow_env\lib\site-packages\tensorflow\python\client\session.py", line 1336, in _do_call
raise type(e)(node_def, op, message)
tensorflow.python.framework.errors_impl.InvalidArgumentError: You must feed a value for placeholder tensor 'Placeholder_1' with dtype double and shape [?,1]
[[Node: Placeholder_1 = Placeholderdtype=DT_DOUBLE, shape=[?,1], _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

Caused by op 'Placeholder_1', defined at:
File "C:\Users\Grégory\eclipse-workspace\tensorflow_project\Udacity_1.py", line 29, in
y_ = tf.placeholder(tf.float64,shape=(None,1)) #specifying dtype to fit the training set
File "C:\python_for_tensorflow\tensorflow_env\lib\site-packages\tensorflow\python\ops\array_ops.py", line 1599, in placeholder
return gen_array_ops._placeholder(dtype=dtype, shape=shape, name=name)
File "C:\python_for_tensorflow\tensorflow_env\lib\site-packages\tensorflow\python\ops\gen_array_ops.py", line 3090, in _placeholder
"Placeholder", dtype=dtype, shape=shape, name=name)
File "C:\python_for_tensorflow\tensorflow_env\lib\site-packages\tensorflow\python\framework\op_def_library.py", line 787, in _apply_op_helper
op_def=op_def)
File "C:\python_for_tensorflow\tensorflow_env\lib\site-packages\tensorflow\python\framework\ops.py", line 2956, in create_op
op_def=op_def)
File "C:\python_for_tensorflow\tensorflow_env\lib\site-packages\tensorflow\python\framework\ops.py", line 1470, in init
self._traceback = self._graph._extract_stack() # pylint: disable=protected-access

InvalidArgumentError (see above for traceback): You must feed a value for placeholder tensor 'Placeholder_1' with dtype double and shape [?,1]
[[Node: Placeholder_1 = Placeholderdtype=DT_DOUBLE, shape=[?,1], _device="/job:localhost/replica:0/task:0/device:CPU:0"]]

---

Do you have any idea on how to fix this?

Thanks a lot!

Greg