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Tensorflow - logistic regrssion -oneHot Encoder - Transformed array of differt size for both train and test

Obtaining consistent one-hot encoding of train / production dataWhy does logistic regression in Spark and R return different models for the same data?Question about train example code for TensorFlowTensorflow oscillating Test and Train Accuracy?Improve test accuracy for TensorFlow CNNHow to properly rotate image and labels for semantic segmentation data augmentation in Tensorflow?Train, test and submission files - what am I supposed to do with all of them?Logistic Regression doesn't predict for the entire test set

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6

$begingroup$

  x_train = tr1.loc[:, ['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']]

#x_train.shape - (120 x 4)



y_train = tr1.loc[:, ['Species']]

#shape - 120 x 3



x_test = test1.loc[:, ['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']]

#shape 30 x 4

y_test = test1.loc[:, ['Species']]

# shape 30 x 3



oneHot = OneHotEncoder()

oneHot.fit(x_train)

# transform

x_train = oneHot.transform(x_train).toarray()

 # fit our y to oneHot encoder

 oneHot.fit(y_train)

 # transform

 y_train = oneHot.transform(y_train).toarray()



 oneHot.fit(x_test)

 # transform

 x_test = oneHot.transform(x_test).toarray()

 # fit our y to oneHot encoder

  oneHot.fit(y_test)

   # transform

  y_test = oneHot.transform(y_test).toarray()



   print("Our features X_test1 in one-hot format")

    print(x_test)

Shape of x_train: (120, 15)
Shape of y_train: (120, 3)
Shape of x_test: (30, 14)
Shape of y_test: (30, 3)

a) After conversion why is the size x_test = 30 x 14 I assume it has to be 30 x 15 ?

# hyperparameters

learning_rate = 0.0001

num_epochs = 100

display_step = 1



# for visualize purpose in tensorboard we use tf.name_scope

with tf.name_scope("Declaring_placeholder"):

# X is placeholdre for iris features. We will feed data later on

x = tf.placeholder(tf.float32, shape=[None, 15])

# y is placeholder for iris labels. We will feed data later on

y = tf.placeholder(tf.float32, shape=[None, 3])

with tf.name_scope("Declaring_variables"):
# W is our weights. This will update during training time
W = tf.Variable(tf.zeros([15, 3]))
# b is our bias. This will also update during training time
b = tf.Variable(tf.zeros([3]))

with tf.name_scope("Declaring_functions"):

# our prediction function

y_ = tf.nn.softmax(tf.add(tf.matmul(x, W), b))

b) did I define x, y, W, b correctly because when I run the accuracy I get this error "ValueError: Cannot feed value of shape (30, 14) for Tensor 'Declaring_placeholder_10/Placeholder:0', which has shape '(?, 15)'
"

tensorflow logistic-regression

share|improve this question

asked yesterday

user80034user80034

3111 bronze badge

New contributor

user80034 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
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$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  See datascience.stackexchange.com/q/54052/55122
  $endgroup$
  – Ben Reiniger
  yesterday
  

  
  
  
  

add a comment | 

6

$begingroup$

  x_train = tr1.loc[:, ['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']]

#x_train.shape - (120 x 4)



y_train = tr1.loc[:, ['Species']]

#shape - 120 x 3



x_test = test1.loc[:, ['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']]

#shape 30 x 4

y_test = test1.loc[:, ['Species']]

# shape 30 x 3



oneHot = OneHotEncoder()

oneHot.fit(x_train)

# transform

x_train = oneHot.transform(x_train).toarray()

 # fit our y to oneHot encoder

 oneHot.fit(y_train)

 # transform

 y_train = oneHot.transform(y_train).toarray()



 oneHot.fit(x_test)

 # transform

 x_test = oneHot.transform(x_test).toarray()

 # fit our y to oneHot encoder

  oneHot.fit(y_test)

   # transform

  y_test = oneHot.transform(y_test).toarray()



   print("Our features X_test1 in one-hot format")

    print(x_test)

Shape of x_train: (120, 15)
Shape of y_train: (120, 3)
Shape of x_test: (30, 14)
Shape of y_test: (30, 3)

a) After conversion why is the size x_test = 30 x 14 I assume it has to be 30 x 15 ?

# hyperparameters

learning_rate = 0.0001

num_epochs = 100

display_step = 1



# for visualize purpose in tensorboard we use tf.name_scope

with tf.name_scope("Declaring_placeholder"):

# X is placeholdre for iris features. We will feed data later on

x = tf.placeholder(tf.float32, shape=[None, 15])

# y is placeholder for iris labels. We will feed data later on

y = tf.placeholder(tf.float32, shape=[None, 3])

with tf.name_scope("Declaring_variables"):
# W is our weights. This will update during training time
W = tf.Variable(tf.zeros([15, 3]))
# b is our bias. This will also update during training time
b = tf.Variable(tf.zeros([3]))

with tf.name_scope("Declaring_functions"):

# our prediction function

y_ = tf.nn.softmax(tf.add(tf.matmul(x, W), b))

b) did I define x, y, W, b correctly because when I run the accuracy I get this error "ValueError: Cannot feed value of shape (30, 14) for Tensor 'Declaring_placeholder_10/Placeholder:0', which has shape '(?, 15)'
"

tensorflow logistic-regression

share|improve this question

asked yesterday

user80034user80034

3111 bronze badge

New contributor

user80034 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

• 
  
  
  

  
  

  
  
  
  
  
  

  
  $begingroup$
  See datascience.stackexchange.com/q/54052/55122
  $endgroup$
  – Ben Reiniger
  yesterday
  

  
  
  
  

add a comment | 

$begingroup$

  x_train = tr1.loc[:, ['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']]

#x_train.shape - (120 x 4)



y_train = tr1.loc[:, ['Species']]

#shape - 120 x 3



x_test = test1.loc[:, ['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']]

#shape 30 x 4

y_test = test1.loc[:, ['Species']]

# shape 30 x 3



oneHot = OneHotEncoder()

oneHot.fit(x_train)

# transform

x_train = oneHot.transform(x_train).toarray()

 # fit our y to oneHot encoder

 oneHot.fit(y_train)

 # transform

 y_train = oneHot.transform(y_train).toarray()



 oneHot.fit(x_test)

 # transform

 x_test = oneHot.transform(x_test).toarray()

 # fit our y to oneHot encoder

  oneHot.fit(y_test)

   # transform

  y_test = oneHot.transform(y_test).toarray()



   print("Our features X_test1 in one-hot format")

    print(x_test)

Shape of x_train: (120, 15)
Shape of y_train: (120, 3)
Shape of x_test: (30, 14)
Shape of y_test: (30, 3)

a) After conversion why is the size x_test = 30 x 14 I assume it has to be 30 x 15 ?

# hyperparameters

learning_rate = 0.0001

num_epochs = 100

display_step = 1



# for visualize purpose in tensorboard we use tf.name_scope

with tf.name_scope("Declaring_placeholder"):

# X is placeholdre for iris features. We will feed data later on

x = tf.placeholder(tf.float32, shape=[None, 15])

# y is placeholder for iris labels. We will feed data later on

y = tf.placeholder(tf.float32, shape=[None, 3])

with tf.name_scope("Declaring_variables"):
# W is our weights. This will update during training time
W = tf.Variable(tf.zeros([15, 3]))
# b is our bias. This will also update during training time
b = tf.Variable(tf.zeros([3]))

with tf.name_scope("Declaring_functions"):

# our prediction function

y_ = tf.nn.softmax(tf.add(tf.matmul(x, W), b))

b) did I define x, y, W, b correctly because when I run the accuracy I get this error "ValueError: Cannot feed value of shape (30, 14) for Tensor 'Declaring_placeholder_10/Placeholder:0', which has shape '(?, 15)'
"

tensorflow logistic-regression

share|improve this question

asked yesterday

user80034user80034

3111 bronze badge

New contributor

user80034 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

$endgroup$

  x_train = tr1.loc[:, ['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']]

#x_train.shape - (120 x 4)



y_train = tr1.loc[:, ['Species']]

#shape - 120 x 3



x_test = test1.loc[:, ['Sepal Length', 'Sepal Width', 'Petal Length', 'Petal Width']]

#shape 30 x 4

y_test = test1.loc[:, ['Species']]

# shape 30 x 3



oneHot = OneHotEncoder()

oneHot.fit(x_train)

# transform

x_train = oneHot.transform(x_train).toarray()

 # fit our y to oneHot encoder

 oneHot.fit(y_train)

 # transform

 y_train = oneHot.transform(y_train).toarray()



 oneHot.fit(x_test)

 # transform

 x_test = oneHot.transform(x_test).toarray()

 # fit our y to oneHot encoder

  oneHot.fit(y_test)

   # transform

  y_test = oneHot.transform(y_test).toarray()



   print("Our features X_test1 in one-hot format")

    print(x_test)

# hyperparameters

learning_rate = 0.0001

num_epochs = 100

display_step = 1



# for visualize purpose in tensorboard we use tf.name_scope

with tf.name_scope("Declaring_placeholder"):

# X is placeholdre for iris features. We will feed data later on

x = tf.placeholder(tf.float32, shape=[None, 15])

# y is placeholder for iris labels. We will feed data later on

y = tf.placeholder(tf.float32, shape=[None, 3])

with tf.name_scope("Declaring_functions"):

# our prediction function

y_ = tf.nn.softmax(tf.add(tf.matmul(x, W), b))

share|improve this question

asked yesterday

user80034user80034

3111 bronze badge

New contributor

user80034 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

share|improve this question

asked yesterday

user80034user80034

3111 bronze badge

New contributor

user80034 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked yesterday

user80034user80034

3111 bronze badge

New contributor

user80034 is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.

asked yesterday

user80034user80034

3111 bronze badge

1 Answer
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6

$begingroup$

Your shape is (30, 14) and not (30, 15) because there are only 14 unique values in your test (one is missing). In any case you shouldn't fit the encoder on the test set, just on the training set. Then just transform on the test set and you'll get the correct dimensions.

Also as far as I can see W and b are declared correctly. I'd ask however you take a minute to format your question a bit better next time.

share|improve this answer

answered yesterday

JcartJcart

30066 bronze badges

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6

$begingroup$

Your shape is (30, 14) and not (30, 15) because there are only 14 unique values in your test (one is missing). In any case you shouldn't fit the encoder on the test set, just on the training set. Then just transform on the test set and you'll get the correct dimensions.

Also as far as I can see W and b are declared correctly. I'd ask however you take a minute to format your question a bit better next time.

share|improve this answer

answered yesterday

JcartJcart

30066 bronze badges

$endgroup$

add a comment | 

6

$begingroup$

Your shape is (30, 14) and not (30, 15) because there are only 14 unique values in your test (one is missing). In any case you shouldn't fit the encoder on the test set, just on the training set. Then just transform on the test set and you'll get the correct dimensions.

Also as far as I can see W and b are declared correctly. I'd ask however you take a minute to format your question a bit better next time.

share|improve this answer

answered yesterday

JcartJcart

30066 bronze badges

$endgroup$

add a comment | 

$begingroup$

Your shape is (30, 14) and not (30, 15) because there are only 14 unique values in your test (one is missing). In any case you shouldn't fit the encoder on the test set, just on the training set. Then just transform on the test set and you'll get the correct dimensions.

Also as far as I can see W and b are declared correctly. I'd ask however you take a minute to format your question a bit better next time.

share|improve this answer

answered yesterday

JcartJcart

30066 bronze badges

$endgroup$

share|improve this answer

answered yesterday

JcartJcart

30066 bronze badges

answered yesterday

JcartJcart

30066 bronze badges

answered yesterday

JcartJcart

30066 bronze badges