UnimplementedError: Graph execution error: running nn on tensorflow

Question

I have been having this error, and I don't know why, especially since I am following someone's code exactly and the person had no error when running this

img_shape = (128,128,3)

# load pretrained model
base_model = tf.keras.applications.VGG19(input_shape=img_shape, include_top=False, weights='imagenet')

# freezing the model
base_model.trainable = False

#define the custom head for network
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()(base_model.output)

# output / prediction layer
prediction_layer = tf.keras.layers.Dense(units=1, activation='sigmoid')(global_average_layer)

model = tf.keras.models.Model(inputs=base_model.input, outputs=prediction_layer)

# compile the model
opt = tf.keras.optimizers.RMSprop(learning_rate=0.0001)
model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])

# create data generators
# import library
from tensorflow.keras.preprocessing.image import ImageDataGenerator

# define objects
data_gen_train = ImageDataGenerator(rescale=1/255.0)
data_gen_test = ImageDataGenerator(rescale=1/255.0)

# define variables
train_generator = data_gen_train.flow_from_directory(directory=training_dir, target_size=(128,128), batch_size=128, class_mode='binary')
test_generator = data_gen_test.flow_from_directory(directory=test_dir, target_size=(128,128), batch_size=128, class_mode='binary')

model.fit_generator(generator=train_generator, epochs=5, validation_data=test_generator)

This is the error I am getting

/usr/local/lib/python3.7/dist-packages/ipykernel_launcher.py:1: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
  """Entry point for launching an IPython kernel.
Epoch 1/5
---------------------------------------------------------------------------
UnimplementedError                        Traceback (most recent call last)
<ipython-input-46-18b18ca5977c> in <module>()
----> 1 model.fit_generator(generator=train_generator, epochs=5, validation_data=test_generator)

2 frames
/usr/local/lib/python3.7/dist-packages/keras/engine/training.py in fit_generator(self, generator, steps_per_epoch, epochs, verbose, callbacks, validation_data, validation_steps, validation_freq, class_weight, max_queue_size, workers, use_multiprocessing, shuffle, initial_epoch)
   2221         use_multiprocessing=use_multiprocessing,
   2222         shuffle=shuffle,
-> 2223         initial_epoch=initial_epoch)
   2224 
   2225   @doc_controls.do_not_generate_docs

/usr/local/lib/python3.7/dist-packages/keras/utils/traceback_utils.py in error_handler(*args, **kwargs)
     65     except Exception as e:  # pylint: disable=broad-except
     66       filtered_tb = _process_traceback_frames(e.__traceback__)
---> 67       raise e.with_traceback(filtered_tb) from None
     68     finally:
     69       del filtered_tb
/usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name)
     53     ctx.ensure_initialized()
     54     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
---> 55                                         inputs, attrs, num_outputs)
     56   except core._NotOkStatusException as e:
     57     if name is not None:

UnimplementedError: Graph execution error:

Detected at node 'model/block1_conv1/Conv2D' defined at (most recent call last):
    File "/usr/lib/python3.7/runpy.py", line 193, in _run_module_as_main
      "__main__", mod_spec)

I could not put in the complete error at the end, but Please, can someone tell me what's wrong

General Grievance · Accepted Answer · 2023-03-17 13:04:35Z

1

it is easy the train_generator is old you can use this.

It is about the memory you create the matrixes initial operations that will reduce the working loads and perform well on data sizes.

[ Sample ]:

import os
from os.path import exists

import tensorflow as tf

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Variables
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
img_shape = (128,128,3)
BATCH_SIZE = 1
IMG_SIZE = (128, 128)

database_buffer = "F:\\models\\buffer\\" + os.path.basename(__file__).split('.')[0] + "\\TF_DataSets_01.h5"
database_buffer_dir = os.path.dirname(database_buffer)

if not exists(database_buffer_dir) : 
    os.mkdir(database_buffer_dir)
    print("Create directory: " + database_buffer_dir)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: DataSets
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
PATH = 'F:\\datasets\\downloads\\cats_name'
train_dir = os.path.join(PATH, 'train')
validation_dir = os.path.join(PATH, 'validation')

train_dataset = tf.keras.utils.image_dataset_from_directory(train_dir,
                                                                        shuffle=True,
                                                                        batch_size=BATCH_SIZE,
                                                                        image_size=IMG_SIZE)

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Model Initialize
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
# load pretrained model
base_model = tf.keras.applications.VGG19(input_shape=img_shape, include_top=False, weights='imagenet')

# freezing the model
base_model.trainable = False

#define the custom head for network
global_average_layer = tf.keras.layers.GlobalAveragePooling2D()(base_model.output)

# output / prediction layer
prediction_layer = tf.keras.layers.Dense(units=1, activation='sigmoid')(global_average_layer)

model = tf.keras.models.Model(inputs=base_model.input, outputs=prediction_layer)
model.summary()

# compile the model
opt = tf.keras.optimizers.RMSprop(learning_rate=0.0001)
model.compile(optimizer=opt, loss='binary_crossentropy', metrics=['accuracy'])

"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
: Training
"""""""""""""""""""""""""""""""""""""""""""""""""""""""""
history = model.fit( train_dataset, batch_size=100, epochs=50 )

[ Output ]:

Sample

edited Mar 17, 2023 at 13:04

General Grievance

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answered Apr 24, 2022 at 4:45

Jirayu Kaewprateep

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2 Comments

Uchechi Ugo Over a year ago

Hi. I tried doing this, and still got a very similar Graph execution error message. I think it has something to do with Google Colab? That is what i'm trying to run it on

Jirayu Kaewprateep Over a year ago

python is running in WIndows OS directly but it should working the same in Google Colab.

Pengyue Xu · Accepted Answer · 2023-03-29 09:21:12Z

1

If you are using colab, try to switch the gpu to tpu. It might helpful

answered Mar 29, 2023 at 9:21

Pengyue Xu

111 bronze badge

Comments

Ali karimi · Accepted Answer · 2025-01-05 16:23:10Z

1

use jit_compile=False, this worked for me:

my_model.compile(optimizer="Adam",...., jit_compile=False)

answered Jan 5 at 16:23

Ali karimi

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Comments

AKSHAY VAYAK · Accepted Answer · 2024-01-18 17:19:05Z

0

The solution to this error is to check the image shape and also you have added shape to the neural network. Both must be the same.

It is solve my problem.

edited Jan 18, 2024 at 17:19

answered Jan 18, 2024 at 17:16

AKSHAY VAYAK

13 bronze badges

1 Comment

Community Over a year ago

As it’s currently written, your answer is unclear. Please edit to add additional details that will help others understand how this addresses the question asked. You can find more information on how to write good answers in the help center.

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