Data Science Asked on August 13, 2021
I’m trying to run the code below in my Jupyter Notebook.
I get:
AttributeError: module ‘tensorflow.python.keras.utils’ has no
attribute ‘to_categorical’
This is code from Kaggle tutorial. I have installed Keras and Tensorflow.
import numpy as np
import pandas as pd
from sklearn.model_selection import train_test_split
from tensorflow.python import keras
from tensorflow.python.keras.models import Sequential
from tensorflow.python.keras.layers import Dense, Flatten, Conv2D, Dropout
img_rows, img_cols = 28, 28
num_classes = 10
def data_prep(raw):
out_y = keras.utils.to_categorical(raw.label, num_classes)
num_images = raw.shape[0]
x_as_array = raw.values[:,1:]
x_shaped_array = x_as_array.reshape(num_images, img_rows, img_cols, 1)
out_x = x_shaped_array / 255
return out_x, out_y
raw_data = pd.read_csv('trainMNIST.csv')
x, y = data_prep(raw_data)
model = Sequential()
model.add(Conv2D(20, kernel_size=(3, 3),
activation='relu',
input_shape=(img_rows, img_cols, 1)))
model.add(Conv2D(20, kernel_size=(3, 3), activation='relu'))
model.add(Flatten())
model.add(Dense(128, activation='relu'))
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer='adam',
metrics=['accuracy'])
model.fit(x, y,
batch_size=128,
epochs=2,
validation_split = 0.2)
Use keras>=2.2 and tensorflow >=1.14 to resolve the issue.
Answered by SrJ on August 13, 2021
Include this in your code
from tensorflow import keras
in place of
from tensorflow.python import keras
Answered by Anubhav Agrawal on August 13, 2021
As it already has been said, to_categorical()
is function. It in keras for tensorflow 2.x can be imported this way:
from keras.utils import to_categorical
then used like this:
digit=6
x=to_categorical(digit, 10)
print(x)
it will print
[0. 0. 0. 0. 0. 0. 1. 0. 0. 0.]
Where 10 is the number of classes, the input values range is [0;number_of_classes-1]. The output is activated (1) or not active (0) position.
Answered by A. Genchev on August 13, 2021
Newer versions of keras==2.4.0 and tensorflow==2.3.0 would work as follows.
Import:
from keras.utils import np_utils
or
from keras import utils as np_utils
and then replace keras.utils.to_categorical
with
keras.utils.np_utils.to_categorical
Answered by Vin Bolisetti on August 13, 2021
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