Meezo ML

20 يوليو 2021

ظهور الخطأ التالي عندما أحاول استخدام fit_generator مع نموذجي:

print(x_train.shape) # (15000, 100, 100, 3)
print(x_test.shape) # (8708, 100, 100, 3)
print(y_train.shape) # (15000, 119)
print(y_test.shape) # (8708, 119)
im=ImageDataGenerator()
data=im.flow(x_train, y_train, 16)
# النموذج
from keras.preprocessing.image import ImageDataGenerator
from keras.layers import AveragePooling2D, MaxPooling2D, Flatten, Conv2D, ZeroPadding2D,Input, Dense, Activation
from keras import layers
from keras.models import Model
x_input = Input((100,100,3))
x = Conv2D(128, (5,5))(x_input)
x = Activation('tanh')(x)
x = MaxPooling2D((3, 3))(x)
x = Conv2D(32, (5,5))(x)
x = Activation('tanh')(x)
x = MaxPooling2D((3, 3))(x)
x = Conv2D(100, (3,3))(x)
x = Activation('tanh')(x)
x = MaxPooling2D((3, 3))(x)
x = Flatten()(x)
x = Dense(256, activation='tanh')(x)
x = Dense(100, activation='tanh')(x)
x = Dense(100, activation='tanh')(x)
output1 = Dense(117, activation='softmax')(x)
output2 = Dense(2, activation='softmax')(x)
model = Model(inputs=x_input, outputs=[output1, output2])
model.compile(optimizer='rmsprop', metrics=['acc'],loss=['categorical_crossentropy'])
model.fit_generator(data, steps_per_epoch=len(x_train) / 16,
                    epochs=5, validation_data=(x_test, y_test))
-------------------------------------------------------------------------------------------------------
ValueError: Error when checking model target: the list of Numpy arrays that you are passing to your model is not the size the model expected.

19 يوليو 2021

أحاول استيراد _obtain_input_shape من الموديول keras.applications.imagenet_utils :

from keras.applications.imagenet_utils import _obtain_input_shape

لكن يظهر لي الخطأ التالي:

ImportError: cannot import name '_obtain_input_shape'

ما المشكلة؟

18 يوليو 2021

قمت بتحميل مجموعة بيانات imdb (مجموعة بيانات لمراجعات الأفلام على الموقع الشهير imdb)لكن البيانات النصية تكون مختلفة الأطوال كما نعلم (مثلاً إحدى المراجعات طولها 100 كلمة و أخرى طولها 20 وهكذا)، كيف يمكننا استخدام التابع pad_sequences لتوحيد أطوالها وتحويلها إلى مصفوفة؟ لأنني أريد تغذية الشبكةالعصبية التالية بها:

model = Sequential()
model.add(Embedding(10000, 8, input_length=maxlen))
model.add(Flatten())
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
model.summary()
history = model.fit(x_train, y_train,
epochs=10,
batch_size=32,
validation_split=0.2)

18 يوليو 2021

ماسبب ظهور الخطأ 'TypeError: __init__() got an unexpected keyword argument 'ragged في الكودالتالي:

path = "E:/keras_model.h5"
from keras.models import load_model
model = load_model(path)
from imutils.video import VideoStream
strem = VideoStream(usePiCamera=True)
from keras.preprocessing.image import img_to_array
import cv2 as cv
import imutils
import numpy
while 1:
    f = strem.Read()
    f = imutils.resize(f, width=600)
    im = cv.resize(f, (32, 32))
    im = im.astype("float32") / 255.0
    im = img_to_array(im)
    im = numpy.expand_dims(im, axis=0)
    (x, rb, whiteBall, none) = model.predict(image)[0]
    l = "none"
    p = none
    if x > none and x > rb and x > wb:
        l = "Fuel"
        p = x
    elif rb > none and rb > fuel and rb > wb:
        l = "Red Ball"
        p = rb
    elif wb > none and wb > rb and wb > x:
        l = "white ball"
        p = wb
    else:
        l = "none"
        p = none
    f = cv.putText(f, "{}:{:.2f%}".format(l, p * 100), (10, 25),cv.FONT_HERSHEY_SIMPLEX, 0.7, (0, 255, 0), 2)
    cv.imshow("Frame", f)
    key = cv.waitKey(1) & 0xFF
    if key == ord("q"):
        break
-----------------------------------------------------------------------------------------------------------------------
Traceback (most recent call last):
  File "/home/pi/Documents/converted_keras/keras-script.py", line 3, in <module>
    model = load_model(MODEL_PATH)
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/saving.py", line 492, in load_wrapper
    return load_function(*args, **kwargs)
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/saving.py", line 584, in load_model
    model = _deserialize_model(h5dict, custom_objects, compile)
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/saving.py", line 274, in _deserialize_model
    model = model_from_config(model_config, custom_objects=custom_objects)
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/saving.py", line 627, in model_from_config
    return deserialize(config, custom_objects=custom_objects)
  File "/usr/local/lib/python3.7/dist-packages/keras/layers/__init__.py", line 168, in deserialize
    printable_module_name='layer')
  File "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py", line 147, in deserialize_keras_object
    list(custom_objects.items())))
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py", line 301, in from_config
    custom_objects=custom_objects)
  File "/usr/local/lib/python3.7/dist-packages/keras/layers/__init__.py", line 168, in deserialize
    printable_module_name='layer')
  File "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py", line 147, in deserialize_keras_object
    list(custom_objects.items())))
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py", line 301, in from_config
    custom_objects=custom_objects)
  File "/usr/local/lib/python3.7/dist-packages/keras/layers/__init__.py", line 168, in deserialize
    printable_module_name='layer')
  File "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py", line 147, in deserialize_keras_object
    list(custom_objects.items())))
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/network.py", line 1056, in from_config
    process_layer(layer_data)
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/network.py", line 1042, in process_layer
    custom_objects=custom_objects)
  File "/usr/local/lib/python3.7/dist-packages/keras/layers/__init__.py", line 168, in deserialize
    printable_module_name='layer')
  File "/usr/local/lib/python3.7/dist-packages/keras/utils/generic_utils.py", line 149, in deserialize_keras_object
    return cls.from_config(config['config'])
  File "/usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py", line 1179, in from_config
    return cls(**config)
  File "/usr/local/lib/python3.7/dist-packages/keras/legacy/interfaces.py", line 91, in wrapper
    return func(*args, **kwargs)
TypeError: __init__() got an unexpected keyword argument 'ragged'

17 يوليو 2021

قمت ببناء نموذج في كيراس، لكن الدقة دوماً تساوي 0 وقيمة الخطأ كبيرة جداً، ما السبب؟

from keras.datasets import boston_housing
(train_data, train_targets), (test_data, test_targets) =boston_housing.load_data()
mean = train_data.mean(axis=0)
train_data -= mean
std = train_data.std(axis=0)
train_data /= std
test_data -= mean
test_data /= std
from keras import models
from keras import layers
model = models.Sequential()
model.add(layers.Dense(64, activation='relu',
input_shape=(train_data.shape[1],)))
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(1))
model.compile(optimizer='rmsprop', loss='mse', metrics=['acc'])
history = model.fit(train_data, train_targets,epochs=14, batch_size=64, verbose=1)
--------------------------------------------------------------------------------------
Epoch 1/14
7/7 [==============================] - 1s 4ms/step - loss: 540.0954 - acc: 0.0000e+00
Epoch 2/14
7/7 [==============================] - 0s 5ms/step - loss: 516.8516 - acc: 0.0000e+00
Epoch 3/14
7/7 [==============================] - 0s 4ms/step - loss: 463.7306 - acc: 0.0000e+00
Epoch 4/14
7/7 [==============================] - 0s 3ms/step - loss: 413.9145 - acc: 0.0000e+00
Epoch 5/14
7/7 [==============================] - 0s 2ms/step - loss: 374.7838 - acc: 0.0000e+00
Epoch 6/14
7/7 [==============================] - 0s 2ms/step - loss: 326.2928 - acc: 0.0000e+00
Epoch 7/14
7/7 [==============================] - 0s 3ms/step - loss: 284.4119 - acc: 0.0000e+00
Epoch 8/14
7/7 [==============================] - 0s 2ms/step - loss: 213.6348 - acc: 0.0000e+00
Epoch 9/14
7/7 [==============================] - 0s 3ms/step - loss: 161.0196 - acc: 0.0000e+00
Epoch 10/14
7/7 [==============================] - 0s 2ms/step - loss: 123.5639 - acc: 0.0000e+00
Epoch 11/14
7/7 [==============================] - 0s 3ms/step - loss: 102.8886 - acc: 0.0000e+00
Epoch 12/14
7/7 [==============================] - 0s 3ms/step - loss: 76.3049 - acc: 0.0000e+00
Epoch 13/14
7/7 [==============================] - 0s 3ms/step - loss: 65.1841 - acc: 0.0000e+00
Epoch 14/14
7/7 [==============================] - 0s 3ms/step - loss: 44.9363 - acc: 0.0000e+00

17 يوليو 2021

قمت ببناء نموذج تصنيف صور واستخدمت الصف Model في بناء النموذج ظهور الخطأ التالي AttributeError: 'Model' object has no attribute 'predict_classes:

m = Sequential()
m.add(Flatten(input_shape=base_model.output_shape[1:]))
m.add(Dense(256, activation='tanh'))
m.add(Dense(3, activation='softmax'))
m.load_weights(top_model_weights_path)
model = Model(inputs=base_model.input, outputs=top_model(base_model.output))
for layer in model.layers[:15]: 
        layer.trainable = False
model.summary()
model.compile(loss='binary_crossentropy', optimizer=SGD(lr=1e-4, momentum=0.99), metrics=['accuracy'])
model.fit_generator(train_generator,
    steps_per_epoch=nb_train_samples // batch_size,
    epochs=epochs,
    validation_data=validation_generator,
    validation_steps=nb_validation_samples // batch_size,
    verbose=1)
model.save_weights(top_model_weights_path)
bottleneck_features_validation = model.predict_generator(validation_generator, nb_validation_samples // batch_size)
np.save(open('bottleneck_features_validation','wb'), bottleneck_features_validation)
validation_data = np.load(open('bottleneck_features_validation', 'rb'))
pred = model.predict_classes(validation_data)

17 يوليو 2021

كيفية حساب كفاءة نماذج التصنيف باستخدام المعيار Accuracy خلال تدريب الشبكة العصبية وبعد الانتهاء من تدريبها؟

17 يوليو 2021

كما قالت في الأعلى هناك مشكلة في اسم المجلد، قم بتغيير الاسم بحيث لايتضمن أحرف خاصة مثل /. أي اجعل اسم المجلد فقط أحرف إنجليزية.

17 يوليو 2021

الشبكات العصبية المتكررة GRUs(Gated recurrent units) والفرق بينها وبين الشبكة LSTM وكيفية استخدامها في Keras؟

15 يوليو 2021

أثناء محاولة استخدام Sequential في Keras يظهر لي الخطأ التالي:

from keras.datasets import imdb
from keras.preprocessing import sequence
from keras import layers
from keras.models import Sequential
max_features = 10000
maxlen = 500
(x_train, y_train), (x_test, y_test) = imdb.load_data(
num_words=max_features)
x_train = [x[::-1] for x in x_train]
x_test = [x[::-1] for x in x_test]
x_train = sequence.pad_sequences(x_train, maxlen=maxlen)
x_test = sequence.pad_sequences(x_test, maxlen=maxlen)
model = Sequential()
model.add(layers.Embedding(max_features, 100))
model.add(layers.Bidirectional(layers.LSTM(64)))
model.add(layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc'])
history = model.fit(x_train, y_train,
epochs=8,
batch_size=64,
validation_split=0.2)
--------------------------------------------------------------------------------------------------------------------
  File "/anaconda/lib/python2.7/site-packages/keras/__init__.py", line 4, in <module>
    from . import applications
  File "/anaconda/lib/python2.7/site-packages/keras/applications/__init__.py", line 1, in <module>
    from .vgg16 import VGG16
  File "/anaconda/lib/python2.7/site-packages/keras/applications/vgg16.py", line 14, in <module>
    from ..models import Model
  File "/anaconda/lib/python2.7/site-packages/keras/models.py", line 14, in <module>
    from . import layers as layer_module
  File "/anaconda/lib/python2.7/site-packages/keras/layers/__init__.py", line 4, in <module>
    from ..engine import Layer
  File "/anaconda/lib/python2.7/site-packages/keras/engine/__init__.py", line 8, in <module>
    from .training import Model
  File "/anaconda/lib/python2.7/site-packages/keras/engine/training.py", line 24, in <module>
    from .. import callbacks as cbks
  File "/anaconda/lib/python2.7/site-packages/keras/callbacks.py", line 25, in <module>
    from tensorflow.contrib.tensorboard.plugins import projector
  File "/anaconda/lib/python2.7/site-packages/tensorflow/contrib/__init__.py", line 30, in <module>
    from tensorflow.contrib import factorization
  File "/anaconda/lib/python2.7/site-packages/tensorflow/contrib/factorization/__init__.py", line 24, in <module>
    from tensorflow.contrib.factorization.python.ops.gmm import *
  File "/anaconda/lib/python2.7/site-packages/tensorflow/contrib/factorization/python/ops/gmm.py", line 27, in <module>
    from tensorflow.contrib.learn.python.learn.estimators import estimator
  File "/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/__init__.py", line 87, in <module>
    from tensorflow.contrib.learn.python.learn import *
  File "/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/__init__.py", line 23, in <module>
    from tensorflow.contrib.learn.python.learn import *
    File "/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/__init__.py", line 25, in <module>
    from tensorflow.contrib.learn.python.learn import estimators
  File "/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/__init__.py", line 297, in <module>
    from tensorflow.contrib.learn.python.learn.estimators.dnn import DNNClassifier
  File "/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn.py", line 29, in <module>
    from tensorflow.contrib.learn.python.learn.estimators import dnn_linear_combined
  File "/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/dnn_linear_combined.py", line 31, in <module>
    from tensorflow.contrib.learn.python.learn.estimators import estimator
  File "/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/estimators/estimator.py", line 49, in <module>
    from tensorflow.contrib.learn.python.learn.learn_io import data_feeder
  File "/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/learn_io/__init__.py", line 21, in <module>
    from tensorflow.contrib.learn.python.learn.learn_io.dask_io import extract_dask_data
  File "/anaconda/lib/python2.7/site-packages/tensorflow/contrib/learn/python/learn/learn_io/dask_io.py", line 26, in <module>
    import dask.dataframe as dd
  File "/anaconda/lib/python2.7/site-packages/dask/dataframe/__init__.py", line 3, in <module>
    from .core import (DataFrame, Series, Index, _Frame, map_partitions,
  File "/anaconda/lib/python2.7/site-packages/dask/dataframe/core.py", line 38, in <module>
    pd.computation.expressions.set_use_numexpr(False)
AttributeError: 'module' object has no attribute 'computation'

15 يوليو 2021

ماهي فكرة الشبكات العصبية المتكررة ثنائية الاتجاه Bidirectional وكيف يتم استخدامها في Keras و TensorFlow؟ وما الفرق بينها وبين الطبقات العادية أحادية الاتجاه مثل LSTM؟

14 يوليو 2021

قمت ببناء نموذج واستخدمت المحسن SGD لكن يظهر لي الخطأ التالي:

from tensorflow.python.keras.layers import Dense,Embedding,LSTM
from tensorflow.python.keras.models import Sequential
from keras.optimizers import SGD
model =Sequential()
model.add(Embedding(max_features, 64))
model.add(LSTM(16))
model.add(Dense(1, activation='sigmoid'))
model.compile(optimizer=SGD(lr=0.01), loss='binary_crossentropy', metrics=['acc'])
history = model.fit(input_train, y_train,
epochs=2,
batch_size=64,
validation_split=0.2)
---------------------------------------------------------------------------------------------------
ValueError: ('Could not interpret optimizer identifier:', )

14 يوليو 2021

ظهر لدي الخطأ التالي أثناء محاولة تدريب نموذج RNN مالسبب:

File "C:\python36-64\lib\site-packages\tensorflow_core\python\keras\engine\training.py", line 734, in fit
    use_multiprocessing=use_multiprocessing)

File "C:\python36-64\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 224, in fit
    distribution_strategy=strategy)

File "C:\python36-64\lib\site-packages\tensorflow_core\python\keras\engine\training_v2.py", line 497, in _process_training_inputs
    adapter_cls = data_adapter.select_data_adapter(x, y)

File "C:\python36-64\lib\site-packages\tensorflow_core\python\keras\engine\data_adapter.py", line 628, in select_data_adapter
    _type_name(x), _type_name(y)))

ValueError: Failed to find data adapter that can handle input: <class 'numpy.ndarray'>, (<class 'list'> containing values of types {"<class 'numpy.float64'>"})

14 يوليو 2021

كيف نقوم بتطبيع البيانات باستخدام الصف Normalizer في Sklearn؟ وما هو مبدأ عمله وما الفرق بينه وبين البقية في Sklearn؟

14 يوليو 2021

أريد توضيح لطبقة LSTM وكيفية استخدامها في كيراس وتنسرفلو؟

13 يوليو 2021

كيفية استخدام المصنف DummyClassifier في Sklearn؟

13 يوليو 2021

طبقة (Recurrent Neural Network) SimpleRNN في Keras و TensorFlow؟

13 يوليو 2021

أقوم ببناء نموذج لكن تظهر لي هذه المشكلة باستمرار، ما الحل؟

from tensorflow.keras.models import Sequential
from tensorflow.keras.initializers import Constant
from tensorflow.python.keras import backend as k
from tensorflow. keras.layers import Flatten, Dropout, Dense,LSTM
from keras.layers.embeddings import Embedding
# تعريف النموذج
model = Sequential()
model.add(Embedding(1000, 128, input_length=512))
model.add(Flatten())
model.add(Dense(4, activation='softmax'))
---------------------------------------------------------------------------------------------
AttributeError: module 'tensorflow' has no attribute 'get_default_graph'

13 يوليو 2021

قمت ببناء نموذج، وبعد أن انتهيت قمت بحفظه:

model.save_weights("WeightsCNN.h5") 
model.save("modelCNN.h5")

لكن عندما أحاول إعادة تحميله تظهر لي المشكلة التالية:

AttributeError: 'str' object has no attribute 'decode'

ما الحل؟

12 يوليو 2021

كيف يمكنني معرفة عدد المعاملات parameters في النموذج في Keras؟

12 يوليو 2021

ظهور الخطأ التالي في Keras أثناء بناء نموذج لتصنيف الصور باستخدام الطبقات التلاففية ValueError: Input 0 is incompatible with layer conv2d_1: expected ndim=4, found ndim=5 علماً أن بيانات التدريب لدي لها الأبعاد التالية:

(26721, 32, 32, 1)

model = Sequential()
model.add(Conv2D(32, (3, 3), padding="same", activation="relu", input_shape=(26721, 32, 32, 1) ))

ما الخطأ؟

12 يوليو 2021

أقرأ في كورس عن التعلم العميق و استخدام مكتبة كيراس وأحاول تطبيق التعليمات في بيئة أناكوندا على جوبيتر لكن يظهر لي الخطأ التالي:

raise ImportError('Could not import PIL.Image. ' ImportError: Could not import PIL.Image. The use of array_to_img requires PIL

رغم أنني قمت بتحميل مكتبة pip install Pillow لكن لم ينجح الأمر.

11 يوليو 2021

ماهي الطبقة Dense layer فيKeras وكيف نقوم ياستخدامها؟

10 يوليو 2021

ظهور الخطأ Out Of Memory (OOM) error في Keras عندما استخدم طبقة التضمين Embedding؟

10 يوليو 2021

ماهي طبقة التضمين Embedding في Keras وكيف نقوم باستخدامها؟

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أجوبة بواسطة Meezo ML