CODEWITHSUNDEEP
pythontensorflowkerascomputer-vision
IROC
IROC

Reputation: 97

Tensorflow fit_generator gives ValueError

I am following a tutorial on tensorflow computer vision. I am new to tensorflow.

conda version : 4.8.3

python version : 3.7.6.final.0

tensorflow : 2.1.0

keras : 2.3.1

The following code is written for a model to recognize rock-paper-scissors from photos of hands. The training as well as test dataset directories are as follows : 

rps ( or rps-test) --
       -- rock
       -- paper
       --scissors

The labels are to be generated from folder names for each pic, as I understood from the tutorial. But code below is giving following error :

Found 2520 images belonging to 3 classes.
Found 372 images belonging to 3 classes.
2020-05-19 12:16:49.623528: I tensorflow/core/platform/cpu_feature_guard.cc:142] Your CPU supports instructions that this TensorFlow binary was not compiled to use: AVX AVX2
WARNING:tensorflow:From c:/Users/IROC/Desktop/FashionMNIST/zip handling.py:55: Model.fit_generator (from tensorflow.python.keras.engine.training) is deprecated and will be removed in a future version.
Instructions for updating:
Please use Model.fit, which supports generators.
WARNING:tensorflow:sample_weight modes were coerced from
  ...
    to
  ['...']
Traceback (most recent call last):
  File "c:/Users/IROC/Desktop/FashionMNIST/zip handling.py", line 55, in <module>
    history = model.fit_generator(train_generator ,epochs = 5, validation_data = validation_datagen, verbose = 1)
  File "C:\Users\IROC\Anaconda3\lib\site-packages\tensorflow_core\python\util\deprecation.py", line 324, in new_func
    return func(*args, **kwargs)
  File "C:\Users\IROC\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 1306, in fit_generator
    initial_epoch=initial_epoch)
  File "C:\Users\IROC\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 819, in fit
    use_multiprocessing=use_multiprocessing)
  File "C:\Users\IROC\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 235, in fit
    use_multiprocessing=use_multiprocessing)
  File "C:\Users\IROC\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 614, in _process_training_inputs
    distribution_strategy=distribution_strategy)
  File "C:\Users\IROC\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 636, in _process_inputs
    adapter_cls = data_adapter.select_data_adapter(x, y)
  File "C:\Users\IROC\Anaconda3\lib\site-packages\tensorflow_core\python\keras\engine\data_adapter.py", line 998, in select_data_adapter
    _type_name(x), _type_name(y)))
ValueError: Failed to find data adapter that can handle input: <class 'tensorflow.python.keras.preprocessing.image.ImageDataGenerator'>, <class 'NoneType'>

import tensorflow as tf
from keras_preprocessing.image.image_data_generator import ImageDataGenerator

import os
import zipfile

training_dir = './datasets/rps/'
validation_dir = './datasets/rps-test-set/'


training_datagen = ImageDataGenerator(rescale = 1./255)
train_generator = training_datagen.flow_from_directory(
    directory = './datasets/rps/',
    target_size = (300,300),
    class_mode = 'categorical'
)


validation_datagen = ImageDataGenerator(rescale = 1./255)
validation_generator = training_datagen.flow_from_directory(
    directory = './datasets/rps-test-set/',
    target_size = (300,300),
    class_mode = 'categorical'
)

model = tf.keras.models.Sequential([
    tf.keras.layers.Conv2D(64, (3,3), activation = 'relu', input_shape = (300,300,3)),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(64, (3,3), activation = 'relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(128, (3,3), activation = 'relu'),
    tf.keras.layers.MaxPooling2D(2,2),
    tf.keras.layers.Conv2D(128,(3,3), activation= 'relu'),
    tf.keras.layers.MaxPooling2D(2,2),

    tf.keras.layers.Flatten(),
    tf.keras.layers.Dropout(0.5),

    tf.keras.layers.Dense(512, activation = 'relu'),
    tf.keras.layers.Dense(3, activation = 'softmax')
])

model.compile(loss = 'categorical_crossentropy', optimizer = 'rmsprop', metrics = ['accuracy'])

history = model.fit_generator(train_generator ,epochs = 5, validation_data = validation_datagen, verbose = 1)

Upvotes: 0

Views: 1159

Answers (1)

florex
florex

Reputation: 963

I think it might be validation_data = validation_generator instead of validation_datagen.

In addition as the error suggest, check if validation_generator and train_generator

are not None.

Upvotes: 2

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