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bad zip file error while loading the trained deep learning model

I trained my model in colab and save it with torch.save('model.pth')
and then when i wanted to load it in my pycharm i get this error:
File "C:\Users\Amin\AppData\Local\Programs\Python\Python310\lib\zipfile.py", line 1334, in _RealGetContents
raise BadZipFile("File is not a zip file")
zipfile.BadZipFile: File is not a zip file`
can anyone help me to fix this error please
i could not find any solution for it on internet
i used tensorflow for training my model and used these imports :
from tensorflow.keras.preprocessing.text import text_to_word_sequence
from tensorflow.keras.preprocessing.text import Tokenizer
from tensorflow.keras.preprocessing import sequence
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense, Dropout, Activation
from tensorflow.keras.layers import Embedding
from tensorflow.keras.layers import Conv1D, GlobalMaxPooling1D
my program can load the tokenizer that i have built but it wont load the model
this is my model :
max_features = 1000
maxlen = 650
embedding_dims = 50
filters = 250
kernel_size = 3
hidden_dims = 250
model5 = Sequential()
model5.add(Embedding(max_features, embedding_dims ))
model5.add(Dropout(0.2))
model5.add(Conv1D(filters, kernel_size, padding='valid', activation='relu', strides=1))
model5.add(GlobalMaxPooling1D())
model5.add(Dense(hidden_dims)) model5.add(Dropout(0.2)) model5.add(Activation('relu'))
model5.add(Dense(5)) model5.add(Activation('softmax'))
model5.compile(loss='categorical_crossentropy',
optimizer='rmsprop',
metrics=['accuracy'])
model5.fit(X_train, y_train,
batch_size=32,
epochs=14,
validation_data=(X_test, y_test))
torch.save(model5,'model.pth')
i loaded my model in colab and it was fine but it didn't work in pycharm
relative_model_path = "model.pth"
full_model_path = os.path.join(absolute_path, relative_model_path)
model = torch.load(full_model_path)
Traceback (most recent call last):
File "C:\\Users\\Amin\\PycharmProjects\\src\\model\\categorizer.py", line 25, in \<module\>
model = torch.load(full_model_path)
File "C:\\Users\\Amin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\serialization.py", line 789, in load
return \_load(opened_zipfile, map_location, pickle_module, \*\*pickle_load_args)
File "C:\\Users\\Amin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\torch\\serialization.py", line 1131, in \_load
result = unpickler.load()
File "C:\\Users\\Amin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\saving\\pickle_utils.py", line 48, in deserialize_model_from_bytecode
raise e
File "C:\\Users\\Amin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\saving\\pickle_utils.py", line 46, in deserialize_model_from_bytecode
model = saving_lib.load_model(filepath)
File "C:\\Users\\Amin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\saving\\experimental\\saving_lib.py", line 196, in load_model
raise e
File "C:\\Users\\Amin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\site-packages\\keras\\saving\\experimental\\saving_lib.py", line 173, in load_model
with zipfile.ZipFile(filepath, "r") as zipfile_to_load:
File "C:\\Users\\Amin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\zipfile.py", line 1267, in __init__
self.\_RealGetContents()
File "C:\\Users\\Amin\\AppData\\Local\\Programs\\Python\\Python310\\lib\\zipfile.py", line 1334, in \_RealGetContents
raise BadZipFile("File is not a zip file")`your text`
zipfile.BadZipFile: File is not a zip file

I just needed to save the model with
keras.save('model')
not torch because the model was built in tensorflow keras

Few errors occured when training the facial expression recognition model

I am working on a project to recognise facial expressions and train the facial expression recognition model by using convolutional neural network(CNN). In this project, I am using Tensorflow 2.4 version and Python 3.8.8 version
The output:
Found 18282 images belonging to 5 classes.
Found 7178 images belonging to 7 classes.
Below is the error that I got:
2023-01-11 00:09:29.625187: I tensorflow/core/platform/cpu_feature_guard.cc:193] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX AVX2
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
c:/Users/Documents/Bachelor of Computer Science/FYP/Code/Program/Backup Test/TrainEmotionDetector.py:53: UserWarning: `Model.fit_generator` is deprecated and will be removed in a future version. Please use `Model.fit`, which supports generators.
emotion_model_info = emotion_model.fit_generator(
Epoch 1/20
2023-01-11 00:09:31.756943: W tensorflow/tsl/framework/cpu_allocator_impl.cc:82] Allocation of 31719424 exceeds 10% of free system memory.
Traceback (most recent call last):
File "c:/Users/Documents/Bachelor of Computer Science/FYP/Code/Program/Backup Test/TrainEmotionDetector.py", line 53, in <module>
emotion_model_info = emotion_model.fit_generator(
File "C:\Users\anaconda3\lib\site-packages\keras\engine\training.py", line 2604, in fit_generator
return self.fit(
File "C:\Users\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 70, in error_handler
raise e.with_traceback(filtered_tb) from None
File "C:\Users\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py", line 52, in quick_execute
tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,
tensorflow.python.framework.errors_impl.InvalidArgumentError: Graph execution error:
Detected at node 'categorical_crossentropy/softmax_cross_entropy_with_logits' defined at (most recent call last):
File "c:/Users/Documents/Code/Program/Backup Test/TrainEmotionDetector.py", line 53, in <module>
emotion_model_info = emotion_model.fit_generator(
File "C:\Users\anaconda3\lib\site-packages\keras\engine\training.py", line 2604, in fit_generator
return self.fit(
File "C:\Users\anaconda3\lib\site-packages\keras\utils\traceback_utils.py", line 65, in error_handler
return fn(*args, **kwargs)
File "C:\Users\anaconda3\lib\site-packages\keras\engine\training.py", line 1650, in fit
tmp_logs = self.train_function(iterator)
File "C:\Users\anaconda3\lib\site-packages\keras\engine\training.py", line 1249, in train_function
return step_function(self, iterator)
File "C:\Users\anaconda3\lib\site-packages\keras\engine\training.py", line 1233, in step_function
outputs = model.distribute_strategy.run(run_step, args=(data,))
File "C:\Users\anaconda3\lib\site-packages\keras\engine\training.py", line 1222, in run_step
outputs = model.train_step(data)
File "C:\Users\anaconda3\lib\site-packages\keras\engine\training.py", line 1024, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File "C:\Users\anaconda3\lib\site-packages\keras\engine\training.py", line 1082, in compute_loss
return self.compiled_loss(
File "C:\Users\anaconda3\lib\site-packages\keras\engine\compile_utils.py", line 265, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File "C:\Users\anaconda3\lib\site-packages\keras\losses.py", line 152, in __call__
losses = call_fn(y_true, y_pred)
File "C:\Users\anaconda3\lib\site-packages\keras\losses.py", line 284, in call
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File "C:\Users\anaconda3\lib\site-packages\keras\losses.py", line 2004, in categorical_crossentropy
return backend.categorical_crossentropy(
File "C:\Users\anaconda3\lib\site-packages\keras\backend.py", line 5538, in categorical_crossentropy
return tf.nn.softmax_cross_entropy_with_logits(
Node: 'categorical_crossentropy/softmax_cross_entropy_with_logits'
logits and labels must be broadcastable: logits_size=[64,7] labels_size=[64,5]
[[{{node categorical_crossentropy/softmax_cross_entropy_with_logits}}]] [Op:__inference_train_function_1181]
2023-01-11 00:09:32.976764: W tensorflow/core/kernels/data/generator_dataset_op.cc:108] Error occurred when finalizing GeneratorDataset iterator: FAILED_PRECONDITION: Python interpreter state is not initialized. The process may be terminated.
[[{{node PyFunc}}]]
Below is the full code:
# import required packages
import cv2
from keras.models import Sequential
from keras.layers import Conv2D, MaxPooling2D, Dense, Dropout, Flatten
from keras.optimizers import Adam
from keras.preprocessing.image import ImageDataGenerator
# Initialize image data generator with rescaling
train_data_gen = ImageDataGenerator(rescale=1./255)
validation_data_gen = ImageDataGenerator(rescale=1./255)
# Preprocess all test images
train_generator = train_data_gen.flow_from_directory(
'data/train',
target_size=(48, 48),
batch_size=64,
color_mode="grayscale",
class_mode='categorical')
# Preprocess all train images
validation_generator = validation_data_gen.flow_from_directory(
'data/test',
target_size=(48, 48),
batch_size=64,
color_mode="grayscale",
class_mode='categorical')
# create model structure
emotion_model = Sequential()
emotion_model.add(Conv2D(32, kernel_size=(3, 3), activation='relu', input_shape=(48, 48, 1)))
emotion_model.add(Conv2D(64, kernel_size=(3, 3), activation='relu'))
emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
emotion_model.add(Dropout(0.25))
emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
emotion_model.add(Conv2D(128, kernel_size=(3, 3), activation='relu'))
emotion_model.add(MaxPooling2D(pool_size=(2, 2)))
emotion_model.add(Dropout(0.25))
emotion_model.add(Flatten())
emotion_model.add(Dense(1024, activation='relu'))
emotion_model.add(Dropout(0.5))
emotion_model.add(Dense(7, activation='softmax'))
cv2.ocl.setUseOpenCL(False)
emotion_model.compile(loss='categorical_crossentropy', optimizer=Adam(learning_rate=0.0001, decay=1e-6), metrics=['accuracy'])
# Train the neural network/model
emotion_model_info = emotion_model.fit_generator(
train_generator,
steps_per_epoch=28709 // 64,
epochs=20,
validation_data=validation_generator,
validation_steps=7178 // 64)
# save model structure in jason file
model_json = emotion_model.to_json()
with open("model/emotion_model.json", "w") as json_file:
json_file.write(model_json)
# save trained model weight in .h5 file
emotion_model.save_weights('model/emotion_model.h5')
I have upgraded the tensorflow to the latest version by using pip install --upgrade tensorflow but nothing works. It is expected to write the saved model into the emotion_model.json and emotion_model.h5. Please help to solve this problem.

logits and labels must be broadcastable:
logits_size=[64,7] labels_size=[64,5]
Well, you kind of gave it away when you mentioned
the first set of images had just 5 distinct labels
while the next set had 7.
Prune two of those label classes and you'll be back in business.

Using Keras Without GPU

I want to train a Neural Network using Keras but when I want to build the model I get the following error
2022-03-14 09:38:10.526372: E tensorflow/stream_executor/cuda/cuda_driver.cc:271] failed call to cuInit: CUDA_ERROR_NO_DEVICE: no CUDA-capable device is detected
2022-03-14 09:38:10.526465: I tensorflow/stream_executor/cuda/cuda_diagnostics.cc:156] kernel driver does not appear to be running on this host (HSKP02): /proc/driver/nvidia/version does not exist
2022-03-14 09:38:10.527391: I tensorflow/core/platform/cpu_feature_guard.cc:151] This TensorFlow binary is optimized with oneAPI Deep Neural Network Library (oneDNN) to use the following CPU instructions in performance-critical operations: AVX2 FMA
To enable them in other operations, rebuild TensorFlow with the appropriate compiler flags.
I tried to solve this error by writing
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = "-1"
before importing Keras but I still get this error. After this error my code fits the data with the validation set with model.fit() but I get another error
Traceback (most recent call last):
File "shallownet_ex.py", line 44, in <module>
H = model.fit(trainX, trainY, validation_data=(testX, testY), batch_size=32, epochs=100, verbose=1)
File ".../venv/lib/python3.8/site-packages/keras/utils/traceback_utils.py", line 67, in error_handler
raise e.with_traceback(filtered_tb) from None
File ".../venv/lib/python3.8/site-packages/tensorflow/python/framework/func_graph.py", line 1147, in autograph_handler
raise e.ag_error_metadata.to_exception(e)
ValueError: in user code:
File ".../venv/lib/python3.8/site-packages/keras/engine/training.py", line 1021, in train_function *
return step_function(self, iterator)
File ".../venv/lib/python3.8/site-packages/keras/engine/training.py", line 1010, in step_function **
outputs = model.distribute_strategy.run(run_step, args=(data,))
File ".../venv/lib/python3.8/site-packages/keras/engine/training.py", line 1000, in run_step **
outputs = model.train_step(data)
File ".../venv/lib/python3.8/site-packages/keras/engine/training.py", line 860, in train_step
loss = self.compute_loss(x, y, y_pred, sample_weight)
File ".../venv/lib/python3.8/site-packages/keras/engine/training.py", line 918, in compute_loss
return self.compiled_loss(
File ".../venv/lib/python3.8/site-packages/keras/engine/compile_utils.py", line 201, in __call__
loss_value = loss_obj(y_t, y_p, sample_weight=sw)
File ".../venv/lib/python3.8/site-packages/keras/losses.py", line 141, in __call__
losses = call_fn(y_true, y_pred)
File ".../venv/lib/python3.8/site-packages/keras/losses.py", line 245, in call **
return ag_fn(y_true, y_pred, **self._fn_kwargs)
File ".../venv/lib/python3.8/site-packages/keras/losses.py", line 1789, in categorical_crossentropy
return backend.categorical_crossentropy(
File ".../venv/lib/python3.8/site-packages/keras/backend.py", line 5083, in categorical_crossentropy
target.shape.assert_is_compatible_with(output.shape)
ValueError: Shapes (None, 4) and (None, 3) are incompatible
The code I'm using looks like this
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ['CUDA_VISIBLE_DEVICES'] = "-1"
from sklearn.preprocessing import LabelBinarizer
from sklearn.model_selection import train_test_split
from sklearn.metrics import classification_report
from preprocesing import ImageToArrayPreprocessor, SimplePreprocesssor
from datasets import SimpleDatasetLoader
from neuralnetworks.conv import ShallowNet
from keras.optimizers import gradient_descent_v2
from imutils import paths
import argparse
ap = argparse.ArgumentParser()
ap.add_argument("-d", "--dataset", required=True, help="path to input dataset")
args = vars(ap.parse_args())
imagePaths = list(paths.list_images(args["dataset"]))
sp = SimplePreprocesssor(32, 32)
iap = ImageToArrayPreprocessor()
sdl = SimpleDatasetLoader(preprocessors=[sp, iap])
(data, labels) = sdl.load(imagePaths, verbose=500)
data = data.astype("float") / 255.0
(trainX, testX, trainY, testY) = train_test_split(data, labels, test_size=0.25, random_state=42)
trainY = LabelBinarizer().fit_transform(trainY)
testY = LabelBinarizer().fit_transform(testY)
opt = gradient_descent_v2.SGD(learning_rate=0.005)
model = ShallowNet.build(width=32, height=32, depth=3, classes=3)
model.compile(loss="categorical_crossentropy", optimizer=opt, metrics=['acc'])
H = model.fit(trainX, trainY, validation_data=(testX, testY), batch_size=32, epochs=100, verbose=1)
The simpleloader is a function that just loads the images and the simplepreprocesor just resizes the images and I think the error is inside the shallownet.py that looks like this
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
from keras.models import Sequential
from keras.layers.convolutional import Conv2D
from keras.layers.core import Activation, Flatten, Dense
from keras import backend as K
class ShallowNet():
#staticmethod
def build(width, height, depth, classes):
model = Sequential()
inputShape = (height, width, depth)
if K.image_data_format() == "channels_first":
inputShape = (depth, height, width)
model.add(Conv2D(32, (3, 3), padding="same", input_shape=inputShape))
model.add(Activation("relu"))
model.add(Flatten())
model.add(Dense(classes))
model.add(Activation("softmax"))
return model
What I deduce is that as my computer doesn't have a GPU I can't perform the training and then I can't fit the model. There is a way to perform this training?

Stuck here while using Keras (2.3.1)

DataSet
import keras
print(keras.__version__)
mnist = keras.datasets.mnist
(x_train,y_train),(x_test,y_test) = mnist.load_data()
normalizing
x_train = keras.utils.normalize(x_train,axis=1)
x_test = keras.utils.normalize(x_test,axis=1)
model
model = keras.models.Sequential()
model.add(keras.layers.Flatten(x_train))
model.add(keras.layers.Dense(128,activation= keras.nn.relu))
model.add(keras.layers.Dense(128,activation= keras.nn.relu))
model.add(keras.layers.Dense(10,activation= keras.nn.softmax))
model.compile(optimizer='adam',
loss='sparse_categorical_crossentropy',
metrics = ['accuracy']
)
model.fit(x_train,y_train,epochs=3)
ERROR:
Using TensorFlow backend.
2.3.1
Traceback (most recent call last):
File "/Users/aditya/Desktop/Desktop/dataScience/Practice/OpenCV/FaceDetect/Hackathon/classMnist.py", line 28, in <module>
model.add(keras.layers.Flatten(x_train))
File "/usr/local/lib/python3.7/site-packages/keras/layers/core.py", line 495, in __init__
self.data_format = K.normalize_data_format(data_format)
File "/usr/local/lib/python3.7/site-packages/keras/backend/tensorflow_backend.py", line 311, in normalize_data_format
data_format = value.lower()
AttributeError: 'numpy.ndarray' object has no attribute 'lower'
The problem is that the Keras can't flatten the x_train. So Do you know why throw this error?

model.add(keras.layers.Flatten(x_train))
The Keras create a network that can't load data.
model.fit(x_train,y_train,epochs=3)
There are data loaded.
So you should edit the first code:
model.add(keras.layers.Flatten())
And your codes have other error:
# wrong
model.add(keras.layers.Dense(128,activation= keras.nn.relu))
# right
model.add(keras.layers.Dense(128,activation= keras.backend.relu))

how do I solve OSError: [Errno 22] Invalid argument:

I am trying to use the following code to train Keras-I3D model from the following link:
https://github.com/srijandas07/i3d
imported modules are
import os
os.environ['KERAS_BACKEND'] = 'tensorflow'
os.environ["CUDA_DEVICE_ORDER"]="PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"]="3"
from keras.layers import Dense, Flatten, Dropout, Reshape
from keras import regularizers
from keras.preprocessing import image
from keras.models import Model, load_model
from keras.applications.vgg16 import preprocess_input
from keras.utils import to_categorical
from keras.optimizers import SGD
from i3d_inception import Inception_Inflated3d, conv3d_bn
from keras.callbacks import ReduceLROnPlateau, ModelCheckpoint, CSVLogger, Callback
from keras.utils import Sequence, multi_gpu_model
import random
import sys
from multiprocessing import cpu_count
import numpy as np
import glob
from skimage.io import imread
import cv2
some definitions
epochs = str(sys.argv[0])
#epochs = 17
model_name = sys.argv[0]
#model_name = model_name
version = sys.argv[0]
num_classes = 35
batch_size = 16
stack_size = 64
DataLoader_video_train = DataLoader_video_train
DataLoader_video_test = DataLoader_video_test
class CustomModelCheckpoint(Callback):
def __init__(self, model_parallel, path):
super(CustomModelCheckpoint, self).__init__()
self.save_model = model_parallel
self.path = path
self.nb_epoch = 0
def on_epoch_end(self, epoch, logs=None):
self.nb_epoch += 1
self.save_model.save(self.path + str(self.nb_epoch) + '.hdf5')
i3d = i3d_modified(weights = 'rgb_imagenet_and_kinetics')
model = i3d.i3d_flattened(num_classes = num_classes)
optim = SGD(lr = 0.01, momentum = 0.9)
there is an issue here with the csvlogger
reduce_lr = ReduceLROnPlateau(monitor='val_loss', factor = 0.1, patience = 10)
csvlogger = CSVLogger('i3d_'+model_name+'.csv')
model.compile(loss = 'categorical_crossentropy', optimizer = optim, metrics = ['accuracy'])
model_checkpoint = CustomModelCheckpoint(model, './weights_'+model_name+'/epoch_')
train_generator = DataLoader_video_train('/train_CS.txt',version, batch_size = batch_size)
test_generator = DataLoader_video_test('/test_CS.txt', version, batch_size = batch_size)
fit generator
model.fit_generator(
generator = train_generator,
#validation_data=val_generator,
epochs = epochs,
steps_per_epoch = 17,
callbacks = [csvlogger, reduce_lr, model_checkpoint],
max_queue_size = 48,
workers = cpu_count() - 2,
use_multiprocessing = True,
)
print(model.evaluate_generator(generator = test_generator))
I get the following error
runfile('D:/Clones/i3d-master/i3d_train.py', wdir='D:/Clones/i3d-master')
Reloaded modules: i3d_inception
C:\Users\sancy\Anaconda3\lib\site-packages\keras\engine\training_generator.py:47: UserWarning: Using a generator with `use_multiprocessing=True` and multiple workers may duplicate your data. Please consider using the`keras.utils.Sequence class.
UserWarning('Using a generator with `use_multiprocessing=True`'
Traceback (most recent call last):
File "<ipython-input-30-8f7b9cc152d8>", line 1, in <module>
runfile('D:/Clones/i3d-master/i3d_train.py', wdir='D:/Clones/i3d-master')
File "C:\Users\sancy\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 827, in runfile
execfile(filename, namespace)
File "C:\Users\sancy\Anaconda3\lib\site-packages\spyder_kernels\customize\spydercustomize.py", line 110, in execfile
exec(compile(f.read(), filename, 'exec'), namespace)
File "D:/Clones/i3d-master/i3d_train.py", line 109, in <module>
use_multiprocessing = True,
File "C:\Users\sancy\Anaconda3\lib\site-packages\keras\legacy\interfaces.py", line 91, in wrapper
return func(*args, **kwargs)
File "C:\Users\sancy\Anaconda3\lib\site-packages\keras\engine\training.py", line 1418, in fit_generator
initial_epoch=initial_epoch)
File "C:\Users\sancy\Anaconda3\lib\site-packages\keras\engine\training_generator.py", line 102, in fit_generator
callbacks.on_train_begin()
File "C:\Users\sancy\Anaconda3\lib\site-packages\keras\callbacks.py", line 132, in on_train_begin
callback.on_train_begin(logs)
File "C:\Users\sancy\Anaconda3\lib\site-packages\keras\callbacks.py", line 1183, in on_train_begin
**self._open_args)
OSError: [Errno 22] Invalid argument: 'i3d_D:/Clones/i3d-master/i3d_train.py.csv'
What am I doing wrong? how do you correctly use str(sys.argv[0]) and CSVLogger?

It looks like the 'i3d_'+ in the filename is causing an invalid filename in csvlogger = CSVLogger('i3d_'+model_name+'.csv'). Try removing the i3d_ prefix.