Meezo ML

خوارزمية التحسين Adam Optimization واستخدامها في Keras؟

أريد استخدام VGG16 ولكن لا أعرف ما المشكلة في عملية استدعاء الموديل على Google Colab: import keras import numpy as np from keras.applications import VGG16 import pandas as pd import matplotlib.pyplot --------------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-5-46922636139b> in <module>() 1 import keras 2 import numpy as np ----> 3 from keras.applications import VGG16 ImportError: cannot import name 'VGG16' from 'keras.applications' (/usr/local/lib/python3.7/dist-packages/keras/applications/__init__.py)

أحاول تدريب شبكة عصبية (هذه الشبكة عبارة عن تجميع طبقات RNN) لكن يظهر لي الخطأ في الكود التالي: from keras.layers import Dense,Embedding,SimpleRNN from keras.datasets import imdb from keras.preprocessing import sequence from keras.models import Sequential max_features = 1000 maxlen = 20 batch_size = 64 print('Loading data...') (input_train, y_train), (input_test, y_test) = imdb.load_data( num_words=max_features) print(len(input_train), 'train sequences') print(len(input_test), 'test sequences') print('Pad sequences (samples x time)') input_train = sequence.pad_sequences(input_train, maxlen=maxlen) input_test = sequence.pad_sequences(input_test, maxlen=maxlen) print('input_train shape:', input_train.shape) model = Sequential() Loading data... 25000 train sequences 25000 test sequences Pad sequences (samples x time) input_train shape: (25000, 20) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-45-586f07f93ca3> in <module> 18 model.add(Embedding(max_features, 16)) 19 model.add(SimpleRNN(16)) ---> 20 model.add(SimpleRNN(16)) 21 model.add(Dense(1, activation='sigmoid')) 22 model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc']) ~\anaconda3\lib\site-packages\tensorflow\python\training\tracking\base.py in _method_wrapper(self, *args, **kwargs) 515 self._self_setattr_tracking = False # pylint: disable=protected-access 516 try: --> 517 result = method(self, *args, **kwargs) 518 finally: 519 self._self_setattr_tracking = previous_value # pylint: disable=protected-access ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\sequential.py in add(self, layer) 221 # If the model is being built continuously on top of an input layer: 222 # refresh its output. --> 223 output_tensor = layer(self.outputs[0]) 224 if len(nest.flatten(output_tensor)) != 1: 225 raise ValueError(SINGLE_LAYER_OUTPUT_ERROR_MSG) ~\anaconda3\lib\site-packages\tensorflow\python\keras\layers\recurrent.py in __call__(self, inputs, initial_state, constants, **kwargs) 658 659 if initial_state is None and constants is None: --> 660 return super(RNN, self).__call__(inputs, **kwargs) 661 662 # If any of `initial_state` or `constants` are specified and are Keras ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in __call__(self, *args, **kwargs) 950 if _in_functional_construction_mode(self, inputs, args, kwargs, input_list): 951 return self._functional_construction_call(inputs, args, kwargs, --> 952 input_list) 953 954 # Maintains info about the `Layer.call` stack. ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _functional_construction_call(self, inputs, args, kwargs, input_list) 1089 # Check input assumptions set after layer building, e.g. input shape. 1090 outputs = self._keras_tensor_symbolic_call( -> 1091 inputs, input_masks, args, kwargs) 1092 1093 if outputs is None: ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _keras_tensor_symbolic_call(self, inputs, input_masks, args, kwargs) 820 return nest.map_structure(keras_tensor.KerasTensor, output_signature) 821 else: --> 822 return self._infer_output_signature(inputs, args, kwargs, input_masks) 823 824 def _infer_output_signature(self, inputs, args, kwargs, input_masks): ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _infer_output_signature(self, inputs, args, kwargs, input_masks) 860 # overridden). 861 # TODO(kaftan): do we maybe_build here, or have we already done it? --> 862 self._maybe_build(inputs) 863 outputs = call_fn(inputs, *args, **kwargs) 864 ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py in _maybe_build(self, inputs) 2683 if not self.built: 2684 input_spec.assert_input_compatibility( -> 2685 self.input_spec, inputs, self.name) 2686 input_list = nest.flatten(inputs) 2687 if input_list and self._dtype_policy.compute_dtype is None: ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\input_spec.py in assert_input_compatibility(input_spec, inputs, layer_name) 221 'expected ndim=' + str(spec.ndim) + ', found ndim=' + 222 str(ndim) + '. Full shape received: ' + --> 223 str(tuple(shape))) 224 if spec.max_ndim is not None: 225 ndim = x.shape.rank ValueError: Input 0 of layer simple_rnn_20 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 16) model.add(Embedding(max_features, 16)) model.add(SimpleRNN(16)) model.add(SimpleRNN(16)) model.add(Dense(1, activation='sigmoid')) model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc']) history = model.fit(input_train, y_train, epochs=2, batch_size=128, validation_split=0.2)

ماهي ال SparseCategoricalCrossentropy في Kears وكيف نقوم استخدامها؟

أقوم باستخدام keras مع ال Embedding على نموذج بسيط ولكن يظهر لدي الخطأ التالي: inputx= np.random.randint(0,40, 4) model = Sequential() model.add(layers.Embedding(input_dim=40, output_dim=32, input_length=4)) model.add(layers.Flatten()) model.add(layers.Dense(units=5, activation='sigmoid')) print(model(inputx)) --------------------------------------------------------------------------------------- ValueError: Input 0 of layer dense_19 is incompatible with the layer: expected axis -1 of input shape to have value 128 but received input with shape (4, 32)

ماهي خوارزمية التحسين RMSprop وكيف يتم استخدامها في تحسين تدرج النماذج في Keras؟

أقوم ببناء نموذج تصنيف متعدد لكن تظهر لي المشكلة التالية عندما أحاول تدريب النموذج، فما هي المشكلة: from keras.datasets import reuters (train_data, train_labels), (test_data, test_labels) = reuters.load_data( num_words=1000) import numpy as np def vectorize_sequences(sequences, dimension=1000): results = np.zeros((len(sequences), dimension)) for i, sequence in enumerate(sequences): results[i, sequence] = 1. return results x_train = vectorize_sequences(train_data) x_test = vectorize_sequences(test_data) from keras import models from keras import layers model = models.Sequential() model.add(layers.Dense(64, activation='relu', input_shape=(1000,))) model.add(layers.Dense(64, activation='relu')) model.add(layers.Dense(46, activation='softmax')) model.compile(optimizer='rmsprop', loss='categorical_crossentropy', metrics=['accuracy']) history = model.fit(x_train, train_labels, epochs=8, batch_size=512, validation_split=0.2) Epoch 1/8 --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-15-00a9a197f4b0> in <module> 24 epochs=8, 25 batch_size=512, ---> 26 validation_split=0.2) ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py in fit(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing) 1098 _r=1): 1099 callbacks.on_train_batch_begin(step) -> 1100 tmp_logs = self.train_function(iterator) 1101 if data_handler.should_sync: 1102 context.async_wait() ~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in __call__(self, *args, **kwds) 826 tracing_count = self.experimental_get_tracing_count() 827 with trace.Trace(self._name) as tm: --> 828 result = self._call(*args, **kwds) 829 compiler = "xla" if self._experimental_compile else "nonXla" 830 new_tracing_count = self.experimental_get_tracing_count() ~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _call(self, *args, **kwds) 869 # This is the first call of __call__, so we have to initialize. 870 initializers = [] --> 871 self._initialize(args, kwds, add_initializers_to=initializers) 872 finally: 873 # At this point we know that the initialization is complete (or less ~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in _initialize(self, args, kwds, add_initializers_to) 724 self._concrete_stateful_fn = ( 725 self._stateful_fn._get_concrete_function_internal_garbage_collected( # pylint: disable=protected-access --> 726 *args, **kwds)) 727 728 def invalid_creator_scope(*unused_args, **unused_kwds): ~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _get_concrete_function_internal_garbage_collected(self, *args, **kwargs) 2967 args, kwargs = None, None 2968 with self._lock: -> 2969 graph_function, _ = self._maybe_define_function(args, kwargs) 2970 return graph_function 2971 ~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _maybe_define_function(self, args, kwargs) 3359 3360 self._function_cache.missed.add(call_context_key) -> 3361 graph_function = self._create_graph_function(args, kwargs) 3362 self._function_cache.primary[cache_key] = graph_function 3363 ~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _create_graph_function(self, args, kwargs, override_flat_arg_shapes) 3204 arg_names=arg_names, 3205 override_flat_arg_shapes=override_flat_arg_shapes, -> 3206 capture_by_value=self._capture_by_value), 3207 self._function_attributes, 3208 function_spec=self.function_spec, ~\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py in func_graph_from_py_func(name, python_func, args, kwargs, signature, func_graph, autograph, autograph_options, add_control_dependencies, arg_names, op_return_value, collections, capture_by_value, override_flat_arg_shapes) 988 _, original_func = tf_decorator.unwrap(python_func) 989 --> 990 func_outputs = python_func(*func_args, **func_kwargs) 991 992 # invariant: `func_outputs` contains only Tensors, CompositeTensors, ~\anaconda3\lib\site-packages\tensorflow\python\eager\def_function.py in wrapped_fn(*args, **kwds) 632 xla_context.Exit() 633 else: --> 634 out = weak_wrapped_fn().__wrapped__(*args, **kwds) 635 return out 636 ~\anaconda3\lib\site-packages\tensorflow\python\framework\func_graph.py in wrapper(*args, **kwargs) 975 except Exception as e: # pylint:disable=broad-except 976 if hasattr(e, "ag_error_metadata"): --> 977 raise e.ag_error_metadata.to_exception(e) 978 else: 979 raise ValueError: in user code: C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:805 train_function * return step_function(self, iterator) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:795 step_function ** outputs = model.distribute_strategy.run(run_step, args=(data,)) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:1259 run return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:2730 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\distribute\distribute_lib.py:3417 _call_for_each_replica return fn(*args, **kwargs) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:788 run_step ** outputs = model.train_step(data) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\engine\training.py:756 train_step y, y_pred, sample_weight, regularization_losses=self.losses) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\engine\compile_utils.py:203 __call__ loss_value = loss_obj(y_t, y_p, sample_weight=sw) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:152 __call__ losses = call_fn(y_true, y_pred) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:256 call ** return ag_fn(y_true, y_pred, **self._fn_kwargs) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper return target(*args, **kwargs) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\losses.py:1537 categorical_crossentropy return K.categorical_crossentropy(y_true, y_pred, from_logits=from_logits) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py:201 wrapper return target(*args, **kwargs) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\backend.py:4833 categorical_crossentropy target.shape.assert_is_compatible_with(output.shape) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\framework\tensor_shape.py:1134 assert_is_compatible_with raise ValueError("Shapes %s and %s are incompatible" % (self, other)) ValueError: Shapes (None, 1) and (None, 46) are incompatible

ماهي ال Categorical Crossentropy في Kears وكيف نقوم استخدامها؟

أقوم بتدريب موديل في keras على minst ولكن يظهر لدي الخطأ التالي خلال تنفيذ الكود: from keras.models import Sequential from keras import layers import tensorflow as tf from tensorflow.keras.datasets import mnist (X_train,y_train),(X_test,y_test)=mnist.load_data() print(X_train.shape,y_train.shape) model = models.Sequential() model.add(layers.Dense(512, activation='relu', input_shape=(60000,28, 28))) model.add(layers.Dense(1, activation='softmax')) model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy']) model.fit(X_train,y_train) (60000, 28, 28) (60000,) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-41-23ec20f8146c> in <module>() 11 model.compile(optimizer='Adam',loss='categorical_crossentropy',metrics=['accuracy']) 12 ---> 13 model.fit(X_train,y_train) 9 frames /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/func_graph.py in wrapper(*args, kwargs) 984 except Exception as e: # pylint:disable=broad-except 985 if hasattr(e, "ag_error_metadata"): --> 986 raise e.ag_error_metadata.to_exception(e) 987 else: 988 raise ValueError: in user code: /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:855 train_function * return step_function(self, iterator) /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:845 step_function outputs = model.distribute_strategy.run(run_step, args=(data,)) /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:1285 run return self._extended.call_for_each_replica(fn, args=args, kwargs=kwargs) /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:2833 call_for_each_replica return self._call_for_each_replica(fn, args, kwargs) /usr/local/lib/python3.7/dist-packages/tensorflow/python/distribute/distribute_lib.py:3608 _call_for_each_replica return fn(*args, kwargs) /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:838 run_step outputs = model.train_step(data) /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/training.py:797 train_step y, y_pred, sample_weight, regularization_losses=self.losses) /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/engine/compile_utils.py:204 call loss_value = loss_obj(y_t, y_p, sample_weight=sw) /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:155 call losses = call_fn(y_true, y_pred) /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:259 call ** return ag_fn(y_true, y_pred, **self._fn_kwargs) /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper return target(*args, **kwargs) /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/losses.py:1644 categorical_crossentropy y_true, y_pred, from_logits=from_logits) /usr/local/lib/python3.7/dist-packages/tensorflow/python/util/dispatch.py:206 wrapper return target(*args, **kwargs) /usr/local/lib/python3.7/dist-packages/tensorflow/python/keras/backend.py:4862 categorical_crossentropy target.shape.assert_is_compatible_with(output.shape) /usr/local/lib/python3.7/dist-packages/tensorflow/python/framework/tensor_shape.py:1161 assert_is_compatible_with raise ValueError("Shapes %s and %s are incompatible" % (self, other)) ValueError: Shapes (32, 1) and (32, 28) are incompatible ما الحل؟

أحاول استخدام الدالة to_categorical على غوغل كولاب لكن يظهر لي الخطأ التالي عندما أحاول استيرادها: from keras.utils import to_categorical y_train=to_categorical(y_train) --------------------------------------------------------------------------- ImportError Traceback (most recent call last) <ipython-input-1-812bfb11e6e7> in <module>() ----> 1 from keras.utils import to_categorical 2 y_train=to_categorical(y_train) ImportError: cannot import name 'to_categorical' from 'keras.utils' (/usr/local/lib/python3.7/dist-packages/keras/utils/__init__.py) --------------------------------------------------------------------------- NOTE: If your import is failing due to a missing package, you can manually install dependencies using either !pip or !apt. To view examples of installing some common dependencies, click the "Open Examples" button below. --------------------------------------------------------------------------- علماً أنني أقوم باستيرادها بنفس الشكل ضمن ال IDE على جهازي؟ما الحل؟

كيف نقوم بتطبيق خوارزميات Batch, Mini Batch & Stochastic Gradient Descent في كيراس؟وما الفرق بينهم؟

ماهي ال Binary Crossentropy وكيف نقوم باستخدامها في Kears؟

أريد بناء شبكة عصبية متكررة RNN باستخدام كيراس، لكن عند محاولتي استيراد مكتبة Keras يظهر لي الخطأ التالي؟! >>> import keras Using TensorFlow backend. ModuleNotFoundError: No module named 'numpy.core._multiarray_umath' ImportError: numpy.core.multiarray failed to import بالرغم من أنني قمت بتثبيت المكتبة ولدي أحدث إصدار من بايثون

كيف نقوم بترميز البيانات الفئوية في مهام التصنيف المتعدد باستخدام التابع to_categorical في مكتبة Keras؟

قمت بإنشاء نموذج باستخدام إطار العمل Keras، لكن عندما حاولت رسم بنية النموذج أعطاني خطأ، رغم أنني قمت بتثبيت الحزم المشار إليها لكن لم ينجح الأمر. الكود: from tensorflow.keras.utils import plot_model plot_model(model, to_file='model_plot.png', show_shapes=True, show_layer_names=True) الخطأ: ImportError: Failed to import pydot. You must install pydot and graphviz for `pydotprint` to work

ما هو ال Sequential Model في Keras، وكيف نستخدمه؟

كيف نقوم بتقسيم البيانات باستخدام أداة Repeated Stratified KFold في مكتبة Sklearn؟

أحاول استخدام الأداة IterativeImputer من مكتبة Sklearn لكي أعالج القيم المفقودة بكن يظهر لي الخطأ التالي دوماً: ImportError: cannot import name 'IterativeImputer' رغم أني جربت أحدث إصدار من Sklearn لكن الأمر لم ينجح! ما الحل؟

كيف نقوم بتقسيم البيانات باستخدام أداة StratifiedKFold في Sklearn؟

أحاول تطبيق أداة GridSearchCV لاختيار أفضل قيم المعاملات لل SVR لكن تظهر لي المشكلة التالية، ما الخطأ: from sklearn.svm import SVR from sklearn.datasets import load_boston from sklearn.model_selection import train_test_split from sklearn.model_selection import GridSearchCV import pandas as pd BostonData = load_boston() X = BostonData.data y = BostonData.target X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0, shuffle =True) SelectedModel = SVR() Selected = {'kernel':('linear', 'rbf'),'C': [0, 0.7, 8]} GridSearchModel = GridSearchCV(SelectedModel,Selected, cv = 2,return_train_score=True) GridSearchModel.fit(X_train, y_train) C:\Users\Windows.10\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: ValueError: C <= 0 FitFailedWarning) C:\Users\Windows.10\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: ValueError: C <= 0 FitFailedWarning) C:\Users\Windows.10\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: ValueError: C <= 0 FitFailedWarning) C:\Users\Windows.10\anaconda3\lib\site-packages\sklearn\model_selection\_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: ValueError: C <= 0 FitFailedWarning)

كيفية استخدام الأداة MissingIndicator في مكتبة Sklearn لمعالجة القيم المفقودة في البيانات؟

كيفية استخدام ال GridSearchCV في مكتبة Sklearn؟

كيفية استخدام الأداة KNNImputer لتنظيف البيانات في مكتبة Sklearn؟

كيف نقوم بتطبيق خوارزمية ََQDA(Quadratic Discriminant Analysis) في مكتبة Sklearn؟

كيفية تطبيق خوارزمية LDA(Linear Discriminant Analysis) في مكتبة Sklearn؟