Meezo ML

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.

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؟

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)

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'

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

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)

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

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

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

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'

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؟

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:', )

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'>"})

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

أريد توضيح لطبقة LSTM وكيفية استخدامها في كيراس وتنسرفلو؟

كيفية استخدام المصنف DummyClassifier في Sklearn؟

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

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'

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؟

كيف يمكنني معرفة عدد المعاملات parameters في النموذج في Keras؟

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؟

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.

ماهي الطبقة Dense layer فيKeras وكيف نقوم ياستخدامها؟

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

ماهي طبقة التضمين Embedding في Keras وكيف نقوم باستخدامها؟