I've got a dataset coming in via
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=validation_split,
subset="training",
seed=seed,
image_size=(img_height, img_width),
batch_size=batch_size)
(Based around code from https://www.tensorflow.org/tutorials/load_data/images with very minor changes to configuration)
I'm converting the eventual model to a TFLite model, which is working, but I think the model's too large for the end device so I'm trying to run post training quantization by supplying a representative_dataset (like https://www.tensorflow.org/lite/performance/post_training_quantization)
However I can't work out how to turn the dataset generated from image_dataset_from_directory into the format expected by representative_dataset
The example provided has
def representative_dataset():
for data in tf.data.Dataset.from_tensor_slices((images)).batch(1).take(100):
yield [data.astype(tf.float32)]
I've tried things like
def representative_dataset():
for data in train_ds.batch(1).take(100):
yield [data.astype(tf.float32)]
but that wasn't it
Looks like
def representative_dataset():
for image_batch, labels_batch in train_ds:
yield [image_batch]
Was what I was looking for, image_batch is already tf.float32
I wasn't able to get tf.keras.preprocessing.image_dataset_from_directory to work, but I had some luck with tf.keras.preprocessing.ImageDataGenerator.
In my case, the images were in the 'images/all' directory. I had to make sure to remove any non-image files (e.g. XML annotations) from that directory.
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.applications.mobilenet import preprocess_input
def representative_dataset():
test_datagen = ImageDataGenerator(preprocessing_function=preprocess_input)
test_generator = test_datagen.flow_from_directory(
'./images',
target_size=(300, 300),
batch_size=1,
classes=['all'],
class_mode='categorical')
for ind in range(len(test_generator.filenames)):
img_with_label = test_generator.next()
yield [np.array(img_with_label[0], dtype=np.float32, ndmin=2)]
Related
I have been battling with my own implementation on my dataset with a different transformer model than the tutorial, and I have been getting this error AttributeError: 'NoneType' object has no attribute 'dtype', when i was starting to train my model. I have been trying to debug for hours, and then I have tried the tutorial from hugging face as it can be found here https://huggingface.co/transformers/v3.2.0/custom_datasets.html. Running this exact code, so I could identify my mistake, also leads to the same error.
!wget http://ai.stanford.edu/~amaas/data/sentiment/aclImdb_v1.tar.gz
!tar -xf aclImdb_v1.tar.gz
from pathlib import Path
def read_imdb_split(split_dir):
split_dir = Path(split_dir)
texts = []
labels = []
for label_dir in ["pos", "neg"]:
for text_file in (split_dir/label_dir).iterdir():
texts.append(text_file.read_text())
labels.append(0 if label_dir is "neg" else 1)
return texts, labels
train_texts, train_labels = read_imdb_split('aclImdb/train')
test_texts, test_labels = read_imdb_split('aclImdb/test')
from sklearn.model_selection import train_test_split
train_texts, val_texts, train_labels, val_labels = train_test_split(train_texts, train_labels, test_size=.2)
from transformers import DistilBertTokenizerFast
tokenizer = DistilBertTokenizerFast.from_pretrained('distilbert-base-uncased')
train_encodings = tokenizer(train_texts, truncation=True, padding=True)
val_encodings = tokenizer(val_texts, truncation=True, padding=True)
test_encodings = tokenizer(test_texts, truncation=True, padding=True)
import tensorflow as tf
train_dataset = tf.data.Dataset.from_tensor_slices((
dict(train_encodings),
train_labels
))
val_dataset = tf.data.Dataset.from_tensor_slices((
dict(val_encodings),
val_labels
))
test_dataset = tf.data.Dataset.from_tensor_slices((
dict(test_encodings),
test_labels
))
from transformers import TFDistilBertForSequenceClassification
model = TFDistilBertForSequenceClassification.from_pretrained('distilbert-base-uncased')
optimizer = tf.keras.optimizers.Adam(learning_rate=5e-5)
model.compile(optimizer=optimizer, loss=model.compute_loss) # can also use any keras loss fn
model.fit(train_dataset.shuffle(1000).batch(16), epochs=3, batch_size=16)
My goal will be to perform multi-label text classification on my own custom dataset, which unfortunately I cannot share for privacy reasons. If anyone could point out what is wrong with this implementation, will be highly appreciated.
There seems to be an error, when you are passing the loss parameter.
model.compile(optimizer=optimizer, loss=model.compute_loss) # can also use any keras loss fn
You don't need to pass the loss parameter, if you want to use the model's built-in loss function.
I was able to train the model with your provided source code by changing mentioned line to:
model.compile(optimizer=optimizer)
or by passing a loss function
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
model.compile(optimizer=optimizer, loss=loss_fn)
transformers version: 4.20.1
Hope it helps.
I am trying to train a dl model with tf.keras. I have 67 classes of images inside the image directory like airports, bookstore, casino. And for each classes i have at least 100 images. The data is from mit indoor scene dataset But when I am trying to train the model, I am constantly getting this error.
tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: Input size should match (header_size + row_size * abs_height) but they differ by 2
[[{{node decode_image/DecodeImage}}]]
[[IteratorGetNext]]
(1) Invalid argument: Input size should match (header_size + row_size * abs_height) but they differ by 2
[[{{node decode_image/DecodeImage}}]]
[[IteratorGetNext]]
[[IteratorGetNext/_7]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_1570]
Function call stack:
train_function -> train_function
I tried to resolve the problem by resizing the image with the resizing layer, also included the labels='inferred' and label_mode='categorical' in the image_dataset_from_directory method and included loss='categorical_crossentropy' in the model compile method. Previously labels and label_model were not set and loss was sparse_categorical_crossentropy which i think is not right. so I changed them as described above.But I am still having problems.
There is one question related to this in stackoverflow but the person did not mentioned how he solved the problem just updated that - My suggestion is to check the metadata of the dataset. It helped to fix my problem. But did not mentioned what metadata to look for or what he did to solve the problem.
The code that I am using to train the model -
import os
import PIL
import numpy as np
import pandas as pd
import tensorflow as tf
from tensorflow.keras.layers import Conv2D, Dense, MaxPooling2D, GlobalAveragePooling2D
from tensorflow.keras.layers import Flatten, Dropout, BatchNormalization, Rescaling
from tensorflow.keras.models import Sequential
from tensorflow.keras.callbacks import ModelCheckpoint, EarlyStopping
from tensorflow.keras.regularizers import l1, l2
import matplotlib.pyplot as plt
import matplotlib.image as mpimg
from pathlib import Path
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '2'
# define directory paths
PROJECT_PATH = Path.cwd()
DATA_PATH = PROJECT_PATH.joinpath('data', 'Images')
# create a dataset
batch_size = 32
img_height = 180
img_width = 180
train = tf.keras.utils.image_dataset_from_directory(
DATA_PATH,
validation_split=0.2,
subset="training",
labels="inferred",
label_mode="categorical",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size
)
valid = tf.keras.utils.image_dataset_from_directory(
DATA_PATH,
validation_split=0.2,
subset="validation",
labels="inferred",
label_mode="categorical",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size
)
class_names = train.class_names
for image_batch, label_batch in train.take(1):
print("\nImage shape:", image_batch.shape)
print("Label Shape", label_batch.shape)
# resize image
resize_layer = tf.keras.layers.Resizing(img_height, img_width)
train = train.map(lambda x, y: (resize_layer(x), y))
valid = valid.map(lambda x, y: (resize_layer(x), y))
# standardize the data
normalization_layer = tf.keras.layers.Rescaling(1./255)
train = train.map(lambda x, y: (normalization_layer(x), y))
valid = valid.map(lambda x, y: (normalization_layer(x), y))
image_batch, labels_batch = next(iter(train))
first_image = image_batch[0]
print("\nImage (min, max) value:", (np.min(first_image), np.max(first_image)))
print()
# configure the dataset for performance
AUTOTUNE = tf.data.AUTOTUNE
train = train.cache().prefetch(buffer_size=AUTOTUNE)
valid = valid.cache().prefetch(buffer_size=AUTOTUNE)
# create a basic model architecture
num_classes = len(class_names)
# initiate a sequential model
model = Sequential()
# CONV1
model.add(Conv2D(filters=64, kernel_size=3, activation="relu",
input_shape=(img_height, img_width, 3)))
model.add(BatchNormalization())
# CONV2
model.add(Conv2D(filters=64, kernel_size=3,
activation="relu"))
model.add(BatchNormalization())
# Pool + Dropout
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.3))
# CONV3
model.add(Conv2D(filters=128, kernel_size=3,
activation="relu"))
model.add(BatchNormalization())
# CONV4
model.add(Conv2D(filters=128, kernel_size=3,
activation="relu"))
model.add(BatchNormalization())
# POOL + Dropout
model.add(MaxPooling2D(pool_size=2))
model.add(Dropout(0.3))
# FC5
model.add(Flatten())
model.add(Dense(128, activation="relu"))
model.add(Dense(num_classes, activation="softmax"))
# compile the model
model.compile(loss="categorical_crossentropy",
optimizer="adam", metrics=['accuracy'])
# train the model
epochs = 25
early_stopping_cb = EarlyStopping(patience=10, restore_best_weights=True)
history = model.fit(train, validation_data=valid, epochs=epochs,
callbacks=[early_stopping_cb], verbose=2)
result = pd.DataFrame(history.history)
print()
print(result.head())
Note -
I just modified the code to make it as simple as possible to reduce the error. The model run for few batches than again got the above error.
Epoch 1/10
732/781 [===========================>..] - ETA: 22s - loss: 3.7882Traceback (most recent call last):
File ".\02_model1.py", line 139, in <module>
model.fit(train, epochs=10, validation_data=valid)
File "C:\Users\BHOLA\anaconda3\lib\site-packages\keras\engine\training.py", line 1184, in fit
tmp_logs = self.train_function(iterator)
File "C:\Users\BHOLA\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 885, in __call__
result = self._call(*args, **kwds)
File "C:\Users\BHOLA\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py", line 917, in _call
return self._stateless_fn(*args, **kwds) # pylint: disable=not-callable
File "C:\Users\BHOLA\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 3039, in __call__
return graph_function._call_flat(
File "C:\Users\BHOLA\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 1963, in _call_flat
return self._build_call_outputs(self._inference_function.call(
File "C:\Users\BHOLA\anaconda3\lib\site-packages\tensorflow\python\eager\function.py", line 591, in call
outputs = execute.execute(
File "C:\Users\BHOLA\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py", line 59, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: 2 root error(s) found.
(0) Invalid argument: Input size should match (header_size + row_size * abs_height) but they differ by 2
[[{{node decode_image/DecodeImage}}]]
[[IteratorGetNext]]
(1) Invalid argument: Input size should match (header_size + row_size * abs_height) but they differ by 2
[[{{node decode_image/DecodeImage}}]]
[[IteratorGetNext]]
[[IteratorGetNext/_2]]
0 successful operations.
0 derived errors ignored. [Op:__inference_train_function_11840]
Function call stack:
train_function -> train_function
Modified code -
# create a dataset
batch_size = 16
img_height = 256
img_width = 256
train = image_dataset_from_directory(
DATA_PATH,
validation_split=0.2,
subset="training",
labels="inferred",
label_mode="categorical",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size
)
valid = image_dataset_from_directory(
DATA_PATH,
validation_split=0.2,
subset="validation",
labels="inferred",
label_mode="categorical",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size
)
model = tf.keras.applications.Xception(
weights=None, input_shape=(img_height, img_width, 3), classes=67)
model.compile(optimizer='rmsprop', loss='categorical_crossentropy')
model.fit(train, epochs=10, validation_data=valid)
I think it might be a corrupted file. It is throwing an exception after a data integrity check in the DecodeBMPv2 function (https://github.com/tensorflow/tensorflow/blob/0b6b491d21d6a4eb5fbab1cca565bc1e94ca9543/tensorflow/core/kernels/image/decode_image_op.cc#L594)
If that's the issue and you want to find out which file(s) are throwing the exception, you can try something like this below on the directory containing the files. Remove/replace any files you find and it should train normally.
import glob
img_paths = glob.glob(os.path.join(<path_to_dataset>,'*/*.*') # assuming you point to the directory containing the label folders.
bad_paths = []
for image_path in img_paths:
try:
img_bytes = tf.io.read_file(path)
decoded_img = tf.io.decode_image(img_bytes)
except tf.errors.InvalidArgumentError as e:
print(f"Found bad path {image_path}...{e}")
bad_paths.append(image_path)
print(f"{image_path}: OK")
print("BAD PATHS:")
for bad_path in bad_paths:
print(f"{bad_path}")
This is in fact a corrupted file problem. However, the underlying issue is far more subtle. Here is an explanation of what is going on and how to circumvent this obstacle. I encountered the very same problem on the very same MIT Indoor Scene Classification dataset. All the images are JPEG files (spoiler alert: well, are they?).
It has been correctly noted that the exception is raised exactly here, in a C++ file related to the tf.io.decode_image() function. It is the decode_image() function where the issue lies, which is called by the
tf.keras.utils.image_dataset_from_directory().
On the other hand, tf.keras.preprocessing.image.ImageDataGenerator().flow_from_directory() relies on Pillow under the hood (shown here, which is called from here). This is the reason why adopting the ImageDataGenerator class works.
After closer inspection of the corresponding C++ source file, one can observe that the function is actually called DecodeBmpV2(...), as defined here. This raises the question of why a JPEG image is being treated as a BMP one. The aforementioned function is actually called here, as part of a basic switch statement the aim of which is further direct data conversion according to the determined type. Thus, the piece of code that determines the file type should be subjected to deeper analysis. The file type is determined according to the value of starting bytes (see here). Long story short, a simple comparison of so-called magic bytes that signify file type is performed.
Here is a code extract with the corresponding magic bytes.
static const char kPngMagicBytes[] = "\x89\x50\x4E\x47\x0D\x0A\x1A\x0A";
static const char kGifMagicBytes[] = "\x47\x49\x46\x38";
static const char kBmpMagicBytes[] = "\x42\x4d";
static const char kJpegMagicBytes[] = "\xff\xd8\xff";
After identifying which files raise the exception, I saw that they were supposed to be JPEG files, however, their starting bytes indicated a BMP format instead.
Here is an example of 3 files and their first 10 bytes.
laundromat\laundry_room_area.jpg
b'ffd8ffe000104a464946'
laundromat\Laundry_Room_Edens1A.jpg
b'ffd8ffe000104a464946'
laundromat\Laundry_Room_bmp.jpg
b'424d3800030000000000'
Look at the last one. It even contains the word bmp in the file name. Why is that so? I do not know. The dataset does contain corrupted image files. Someone probably converted the file from BMP to JPEG, yet the tool used did not work correctly. We can just guess the real reason, but that is now irrelevant.
The method by which the file type is determined is different from the one performed by the Pillow package, thus, there is nothing we can do about it. The recommendation is to identify the corrupted files, which is actually easy or to rely on the ImageDataGenerator. However, I would advise against doing so as this class has been marked as deprecated. It is not a bug in code per se, but rather bad data inadvertently introduced into the dataset.
I am fairly new to tensor flow and I am trying to train a BERT model for a binary classification task.
I have a data set in a single CSV file that looks like this:
Description
Target
This text passed
1
This text failed
0
I loaded the data set as a pandas data frame.
The guide I am using is the official tensorflow guide I found here.
The guide uses the IMDb dataset that is structured in separate folders.
This is the code block that created the TensorFlow dataset:
AUTOTUNE = tf.data.AUTOTUNE
batch_size = 32
seed = 42
raw_train_ds = tf.keras.preprocessing.text_dataset_from_directory(
'aclImdb/train',
batch_size=batch_size,
validation_split=0.2,
subset='training',
seed=seed)
class_names = raw_train_ds.class_names
train_ds = raw_train_ds.cache().prefetch(buffer_size=AUTOTUNE)
val_ds = tf.keras.preprocessing.text_dataset_from_directory(
'aclImdb/train',
batch_size=batch_size,
validation_split=0.2,
subset='validation',
seed=seed)
val_ds = val_ds.cache().prefetch(buffer_size=AUTOTUNE)
test_ds = tf.keras.preprocessing.text_dataset_from_directory(
'aclImdb/test',
batch_size=batch_size)
test_ds = test_ds.cache().prefetch(buffer_size=AUTOTUNE)
My question: is there a way to convert my Pandas dataframe into the same format?
I.e How do I generate train_ds, test_ds, and val_ds from a pandas data frame?
I'm trying to implement an Autoencoder in Tensorflow 2.3. I am taking my own Image dataset stored on disk as input.can someone explain to me how this can be done in a correct way?
I tried loading the data in directory using tf.keras.preprocessing.image_dataset_from_directory() but when I use start training with the data taken from above method I am getting following error.
"ValueError: y argument is not supported when using dataset as input."
PFB the code that I am running
'''
import tensorflow as tf
from convautoencoder import ConvAutoencoder
from tensorflow.keras.optimizers import Adam
import matplotlib.pyplot as plt
import numpy as np
EPOCHS = 25
batch_size = 1
img_height = 180
img_width = 180
data_dir = "/media/aniruddha/FE47-91B8/Laptop_Backup/Auto-Encoders/Basic/data"
train_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="training",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
val_ds = tf.keras.preprocessing.image_dataset_from_directory(
data_dir,
validation_split=0.2,
subset="validation",
seed=123,
image_size=(img_height, img_width),
batch_size=batch_size)
(encoder, decoder, autoencoder) = ConvAutoencoder.build(224, 224, 3)
opt = Adam(lr=1e-3)
autoencoder.compile(loss="mse", optimizer=opt)
H = autoencoder.fit( train_ds, train_ds, validation_data=(val_ds, val_ds), epochs=EPOCHS, batch_size=batch_size)
'''
I resolved this. I was not feeding the input dataset as a tuple to the model for training. Once I corrected that the training started.
I used generators to feed the input data as tuple to the autoencoder.
Please find my code below.
# initialize the training training data augmentation object
trainAug = ImageDataGenerator(rescale=1. / 255)
valAug = ImageDataGenerator(rescale=1. / 255)
# initialize the training generator
trainGen = trainAug.flow_from_directory(
config.TRAIN_PATH,
class_mode="input",
classes=None,
target_size=(64, 64),
color_mode="grayscale",
shuffle=True,
batch_size=BS)
# initialize the validation generator
valGen = valAug.flow_from_directory(
config.TRAIN_PATH,
class_mode="input",
classes=None,
target_size=(64, 64),
color_mode="grayscale",
shuffle=False,
batch_size=BS)
# initialize the testing generator
testGen = valAug.flow_from_directory(
config.TRAIN_PATH,
class_mode="input",
classes=None,
target_size=(64, 64),
color_mode="grayscale",
shuffle=False,
batch_size=BS)
early_stop = EarlyStopping(monitor='val_loss', patience=20)
mc = ModelCheckpoint('best_model_1.h5', monitor='val_loss', mode='min', save_best_only=True)
# construct our convolutional autoencoder
print("[INFO] building autoencoder...")
(encoder, decoder, autoencoder) = ConvAutoencoder.build(64, 64, 1)
opt = Adam(learning_rate= 0.0001, beta_1=0.9, beta_2=0.999, epsilon=1e-04, amsgrad=False)
autoencoder.compile(loss="mse", optimizer=opt)
# train the convolutional autoencoder
H = autoencoder.fit( trainGen, validation_data=valGen, epochs=EPOCHS, batch_size=BS ,callbacks=[ mc , early_stop])
fit is expecting data and labels, but it only accepts a single tf.data.Dataset. To use data as labels for the autoencoder you should provide it twice to the dataset constructor, e.g. :
dataset = tf.data.Dataset.from_tensor_slices((images, images))
little background: I'm making a simple rock, paper, scissors image classifier program. Basically, I want the image classifier to be able to distinguish between a rock, paper, or scissor image.
problem: The program works amazing for two of the classes, rock and paper, but completely fails whenever given a scissors test image. I've tried increasing my training data and a few other things but no luck. I was wondering if anyone has any ideas on how to offset this.
sidenote: I suspect it also has something to do with overfitting. I say this because the model has about a 92% accuracy with the training data but 55% accuracy on test data.
import numpy as np
import os
import cv2
import random
import tensorflow as tf
from tensorflow import keras
CATEGORIES = ['rock', 'paper', 'scissors']
IMG_SIZE = 400 # The size of the images that your neural network will use
CLASS_SIZE = len(CATEGORIES)
TRAIN_DIR = "../Train/"
def loadData( directoryPath ):
data = []
for category in CATEGORIES:
path = os.path.join(directoryPath, category)
class_num = CATEGORIES.index(category)
for img in os.listdir(path):
try:
img_array = cv2.imread(os.path.join(path, img), cv2.IMREAD_GRAYSCALE)
new_array = cv2.resize(img_array, (IMG_SIZE, IMG_SIZE))
data.append([new_array, class_num])
except Exception as e:
pass
return data
training_data = loadData(TRAIN_DIR)
random.shuffle(training_data)
X = [] #features
y = [] #labels
for i in range(len(training_data)):
features = training_data[i][0]
label = training_data[i][1]
X.append(features)
y.append(label)
X = np.array(X)
y = np.array(y)
X = X/255.0
model = keras.Sequential([
keras.layers.Flatten(input_shape=(IMG_SIZE, IMG_SIZE)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dense(CLASS_SIZE)
])
model.compile(optimizer='adam',
loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
metrics=['accuracy'])
model.fit(X, y, epochs=25)
TEST_DIR = "../Test/"
test_data = loadData( TEST_DIR )
random.shuffle(test_data)
test_images = []
test_labels = []
for i in range(len(test_data)):
features = test_data[i][0]
label = test_data[i][1]
test_images.append(features)
test_labels.append(label)
test_images = np.array(test_images)
test_images = test_images/255.0
test_labels = np.array(test_labels)
test_loss, test_acc = model.evaluate(test_images, test_labels, verbose=2)
print('\nTest accuracy:', test_acc)
# Saving the model
model_json = model.to_json()
with open("model.json", "w") as json_file :
json_file.write(model_json)
model.save_weights("model.h5")
print("Saved model to disk")
model.save('CNN.model')
If you want to create a massive amount of training data fast: https://github.com/ThomasStuart/RockPaperScissorsMachineLearning/blob/master/source/0.0-collectMassiveData.py
Thanks in advance to any help or ideas :)
You can simply test overfitting by adding 2 additional layers, one dropout layer and one dense layer. Also be sure to shuffle your train_data after each epoch, so the model keeps the learning general. Also, if I see this correctly, you are doing a multi class classification but do not have a softmax activation in the last layer. I would recommend you, to use it.
With drouput and softmax your model would look like this:
model = keras.Sequential([
keras.layers.Flatten(input_shape=(IMG_SIZE, IMG_SIZE)),
keras.layers.Dense(128, activation='relu'),
keras.layers.Dropout(0.4), #0.4 means 40% of the neurons will be randomly unused
keras.layers.Dense(CLASS_SIZE, activation="softmax")
])
As last advice: Cnns perform in general way better with tasks like this. You might want to switch to a CNN network, for having even better performance.