Meezo ML

قمت ببناء نموذج تصنيف متعدد على مجمةعة بيانات x-ray scans لكن لا أفهم سبب الخطأ التالي في الكود: # الكود model = Sequential() model.add(Conv2D(512, (3, 3), input_shape=X_train.shape[1:])) model.add(Activation('tanh')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Conv2D(128, (3, 3))) model.add(Activation('tanh')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Flatten()) model.add(Dense(1000)) model.add(Dense(64)) model.add(Dense(6)) model.add(Activation('softmax')) model.compile(optimizer='rmsprop',loss='categorical_crossentropy',metrics=['acc']) model.fit(X_train, y_label, batch_size=64, epochs=20, validation_split=0.2) ------------------------------------------------------------------------------------------------- ValueError: Shapes (None, 1) and (None, 6) are incompatible

قمت ببناء نموذج Baseline لتصنيف الأصوات، الداتاسيت التي أعمل عليها هي مجموعة بيانات صوتية، أبعادها (400,50,95) # النموذج model = Sequential() model.add(LSTM(64, input_shape = (50,95,1), return_sequences=True)) model.add(LSTM(32,activation='relu')) model.add(Dense(4, activation='softmax')) model.compile(optimizer='rmsprop',loss='categorical_crossentropy', metrics=["acc"]) model.summary() model.fit(X_train, y_train, batch_size=64, nb_epoch=20, validation_data=(X_test, y_test)) لكن يظهر لي الخطأ: ValueError: Shapes (None, 1) and (None, 4) are incompatible

اعمل على تصنيف الصور minst في keras على google colab ولكن قمت ببعض التعديلات على الداتا أي فقط صنفين 0 أو 1 ولكن يظهر لي الخطأ التالي الكود: import numpy as np import pandas as pd from keras.models import Sequential from keras.layers import Dense , Activation, Dropout from keras.models import Sequential from keras.utils.np_utils import to_categorical from keras.datasets import mnist (x_train, y_train),(x_test, y_test) = mnist.load_data() y_train[y_train<=5]=0 y_train[y_train>5]=1 y_train=y_train.astype('int64') print(x_train.shape, y_train.shape) image_size = x_train.shape[1] input_size = image_size * image_size x_train = np.reshape(x_train, [-1, input_size]) x_train = x_train.astype('float32') / 255 x_test = np.reshape(x_test, [-1, input_size]) x_test = x_test.astype('float32') / 255 batch_size = 32 hidden_units = 256 model = Sequential() model.add(Dense(hidden_units, input_shape=(input_size,))) model.add(Activation('relu')) model.add(Dropout(0.45)) model.add(Dense(hidden_units)) model.add(Activation('relu')) model.add(Dropout(0.45)) model.add(Dense(2)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(x_train, y_train, epochs=5, batch_size=batch_size) الخطأ (60000, 28, 28) (60000,) Epoch 1/5 --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-22-3c7ca6e80a5b> in <module>() 32 model.add(Activation('softmax')) 33 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) ---> 34 model.fit(x_train, y_train, epochs=5, batch_size=batch_size) 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/keras/engine/training.py:830 train_function * return step_function(self, iterator) /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:813 run_step * outputs = model.train_step(data) /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:771 train_step * loss = self.compiled_loss( /usr/local/lib/python3.7/dist-packages/keras/engine/compile_utils.py:201 __call__ * loss_value = loss_obj(y_t, y_p, sample_weight=sw) /usr/local/lib/python3.7/dist-packages/keras/losses.py:142 __call__ * losses = call_fn(y_true, y_pred) /usr/local/lib/python3.7/dist-packages/keras/losses.py:246 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/keras/losses.py:1631 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/keras/backend.py:4827 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, 2) are incompatible

قمت ببناء نموذج لتوقع أسعار المنازل باستخدام إطار العمل كيراس لكن ظهر لي الخطأ التالي: from keras.datasets import boston_housing import keras (train_data, train_targets), (test_data, test_targets) = boston_housing.load_data() # توحيد البيانات 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 # بناء النموذج def build_model(): model = models.Sequential() model.add(layers.Dense(64, activation='relu', input_shape=(300,))) model.add(layers.Dense(64, activation='relu')) model.add(layers.Dense(1)) model.compile(optimizer='rmsprop', loss="mse", metrics=['mae']) #بالشكل التالي compile هنا استخدمناها كدالة تكلفة وكمعيار عن طريق تمريره إلى الدالة #model.compile(optimizer='rmsprop', loss='mse', metrics=['mse']) return model # تدريب النموذج model = build_model() model.fit(train_data, train_targets,epochs=8, batch_size=64) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-14-2add221cc354> in <module> 24 # تدريب النموذج 25 model = build_model() ---> 26 model.fit(train_data, train_targets,epochs=8, batch_size=64) ~\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:754 train_step y_pred = self(x, training=True) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\engine\base_layer.py:998 __call__ input_spec.assert_input_compatibility(self.input_spec, inputs, self.name) C:\Users\Windows.10\anaconda3\lib\site-packages\tensorflow\python\keras\engine\input_spec.py:259 assert_input_compatibility ' but received input with shape ' + display_shape(x.shape)) ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 255 but received input with shape (None, 13)

استخدام دالة الخسارة Hinge losses في كيراس؟

ماهي الدالة (MAE) Mean Absolute Error وكيف نستخدمها في Keras؟

ماهي ال logcosh وكيف نقوم باستخدامها في Keras؟

أقوم ببناء شبكة عصبية لمهمة NLP (مهمة تحليل مشاعر على بيانات imdb) لكن لا أعرف سبب الخطأ التالي: from keras.datasets import imdb from keras.layers import Embedding, SimpleRNN,Flatten,Dense from keras.models import Sequential (input_train, y_train), (input_test, y_test) = imdb.load_data( num_words=10000) print(len(input_train), 'train sequences') print(len(input_test), 'test sequences') ################ نضيف################### from keras.preprocessing import sequence maxlen = 20 print('Pad sequences (samples x time)') input_train = sequence.pad_sequences(input_train, maxlen=maxlen) input_test = sequence.pad_sequences(input_test, maxlen=maxlen) ############# انتهى#################### print('input_train shape:', input_train.shape) print('input_test shape:', input_test.shape) from keras.layers import Dense model = Sequential() model.add(Embedding(10000, 16)) model.add(Flatten()) model.add(Dense(32, activation='relu')) 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=32, validation_split=0.2) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-10-08cd97ead789> in <module> 19 model.add(Embedding(10000, 16)) 20 model.add(Flatten()) ---> 21 model.add(Dense(32, activation='relu')) 22 model.add(Dense(1, activation='sigmoid')) 23 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\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) 2708 # operations. 2709 with tf_utils.maybe_init_scope(self): -> 2710 self.build(input_shapes) # pylint:disable=not-callable 2711 # We must set also ensure that the layer is marked as built, and the build 2712 # shape is stored since user defined build functions may not be calling ~\anaconda3\lib\site-packages\tensorflow\python\keras\layers\core.py in build(self, input_shape) 1180 last_dim = tensor_shape.dimension_value(input_shape[-1]) 1181 if last_dim is None: -> 1182 raise ValueError('The last dimension of the inputs to `Dense` ' 1183 'should be defined. Found `None`.') 1184 self.input_spec = InputSpec(min_ndim=2, axes={-1: last_dim}) ValueError: The last dimension of the inputs to `Dense` should be defined. Found `None`.

قمت ببناء نموذج لتحليل المشاعر، لكن عند التدريب يظهر لي الخطأ التالي: df_train = pd.read_csv('/content/drive/MyDrive/imdbdataset/Completely_clean_data.csv') df_train.drop(df_train.filter(regex="Unname"),axis=1, inplace=True) df_test = pd.read_csv('/content/drive/MyDrive/imdbdataset/Completely_clean_data_test.csv') df_test.drop(df_test.filter(regex="Unname"),axis=1, inplace=True) max_words = 75000 tokenizer = Tokenizer(num_words=max_words) # fitting tokenizer.fit_on_texts(pd.concat([df_test['review'], df_train['review']])) #max_len=int(df["review_len"].mean()) #231 # do you remember!! train = tokenizer.texts_to_sequences(df_train['review']) test = tokenizer.texts_to_sequences(df_test['review']) train = pad_sequences(train, maxlen=200) test = pad_sequences(test, maxlen=200) print("the shape of data train :",train.shape) print("the shape of data test :",test.shape) # model def modelBiLSTM(): max_words = 75000 #drop_lstm =0.4 embeddings=128 model = Sequential() model.add(Embedding(10000, embeddings)) model.add(Bidirectional(LSTM(64, activation='tanh'))) # 2D output model.add(Dense(1, activation='sigmoid')) # binary output return model model=modelBiLSTM() model.summary() # training model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc']) history=model.fit(train, train_label,validation_split=0.12, batch_size=32, epochs=8) --------------------------------------------------------------------------- InvalidArgumentError Traceback (most recent call last) <ipython-input-13-850a3e320f47> in <module>() 1 model.compile(optimizer='rmsprop', loss='binary_crossentropy', metrics=['acc']) ----> 2 history=model.fit(train, train_label,validation_split=0.12, batch_size=32, epochs=8) 6 frames /usr/local/lib/python3.7/dist-packages/tensorflow/python/eager/execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 58 ctx.ensure_initialized() 59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, ---> 60 inputs, attrs, num_outputs) 61 except core._NotOkStatusException as e: 62 if name is not None: InvalidArgumentError: indices[28,152] = 57171 is not in [0, 10000) [[node sequential_2/embedding_2/embedding_lookup (defined at /usr/local/lib/python3.7/dist-packages/keras/layers/embeddings.py:184) ]] [Op:__inference_train_function_12661] Errors may have originated from an input operation. Input Source operations connected to node sequential_2/embedding_2/embedding_lookup: sequential_2/embedding_2/embedding_lookup/10350 (defined at /usr/lib/python3.7/contextlib.py:112) sequential_2/embedding_2/Cast (defined at /usr/local/lib/python3.7/dist-packages/keras/layers/embeddings.py:183) Function call stack: train_function

ماهي دالة التكلفة Huber loss وكيف يتم تطبيقها في كيراس Keras؟

ماهو ال MeanSquaredError وكيف نقوم باستخدام لمهام التوقع Regression في Keras؟ وهل يصح استخدامه كمعيار لقياس كفاءة النموذج؟

هل يمكننا استخدام تشابه جيب التمام CosineSimilarity كدالة تكلفة لمهام التوقع Regression في Keras؟

أقوم بتدريب شبكة CNN بسيطة على مجموعة minst في كيراس ولكن لا أعرف لماذا يظهر هذا الخطأ: import numpy as np import keras from keras.models import Sequential from keras.layers import Dense , Activation, Dropout from keras.optimizers import Adam ,RMSprop from keras.models import Sequential from keras.utils.np_utils import to_categorical from keras.datasets import mnist (x_train, y_train),(x_test, y_test) = mnist.load_data() model = keras.models.Sequential([ keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28,28,1)), keras.layers.MaxPool2D(2,2), keras.layers.Conv2D(64, (3,3), activation='relu'), keras.layers.MaxPool2D(2,2), keras.layers.Flatten(), keras.layers.Dense(128, activation='relu'), keras.layers.Dense(10, activation='softmax') ]) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(x_train, y_train, epochs=20,validation_data=(x_test, y_test), batch_size=60) # الخطأ Epoch 1/20 --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-4-30f7b9447993> in <module>() 22 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) 23 ---> 24 model.fit(x_train, y_train, epochs=20,validation_data=[x_test, y_test], batch_size=60) 25 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/keras/engine/training.py:830 train_function * return step_function(self, iterator) /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:813 run_step * outputs = model.train_step(data) /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:770 train_step * y_pred = self(x, training=True) /usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:989 call * input_spec.assert_input_compatibility(self.input_spec, inputs, self.name) /usr/local/lib/python3.7/dist-packages/keras/engine/input_spec.py:227 assert_input_compatibility * raise ValueError('Input ' + str(input_index) + ' of layer ' + ValueError: Input 0 of layer sequential_1 is incompatible with the layer: : expected min_ndim=4, found ndim=3. Full shape received: (60, 28, 28)

خوارزمية التحسين Adamax Optimization واستخدامها في Keras؟

أحاول تدريب شبكة عصبية لتحليل المشاعر، لكن يظهر لي الخطأ التالي: from keras.datasets import imdb from keras.layers import Embedding, SimpleRNN from keras.models import Sequential (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('input_train shape:', input_train.shape) print('input_test shape:', input_test.shape) from keras.layers import Dense model = Sequential() model.add(Embedding(10000, 32)) model.add(SimpleRNN(32)) 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=32, validation_split=0.2) --------------------------------------------------------------------------- ValueError Traceback (most recent call last) <ipython-input-53-0d042e98c73b> in <module> 17 epochs=2, 18 batch_size=32, ---> 19 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) 1062 use_multiprocessing=use_multiprocessing, 1063 model=self, -> 1064 steps_per_execution=self._steps_per_execution) 1065 1066 # Container that configures and calls `tf.keras.Callback`s. ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in __init__(self, x, y, sample_weight, batch_size, steps_per_epoch, initial_epoch, epochs, shuffle, class_weight, max_queue_size, workers, use_multiprocessing, model, steps_per_execution) 1110 use_multiprocessing=use_multiprocessing, 1111 distribution_strategy=ds_context.get_strategy(), -> 1112 model=model) 1113 1114 strategy = ds_context.get_strategy() ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in __init__(self, x, y, sample_weights, sample_weight_modes, batch_size, epochs, steps, shuffle, **kwargs) 261 **kwargs): 262 super(TensorLikeDataAdapter, self).__init__(x, y, **kwargs) --> 263 x, y, sample_weights = _process_tensorlike((x, y, sample_weights)) 264 sample_weight_modes = broadcast_sample_weight_modes( 265 sample_weights, sample_weight_modes) ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in _process_tensorlike(inputs) 1014 return x 1015 -> 1016 inputs = nest.map_structure(_convert_numpy_and_scipy, inputs) 1017 return nest.list_to_tuple(inputs) 1018 ~\anaconda3\lib\site-packages\tensorflow\python\util\nest.py in map_structure(func, *structure, **kwargs) 657 658 return pack_sequence_as( --> 659 structure[0], [func(*x) for x in entries], 660 expand_composites=expand_composites) 661 ~\anaconda3\lib\site-packages\tensorflow\python\util\nest.py in <listcomp>(.0) 657 658 return pack_sequence_as( --> 659 structure[0], [func(*x) for x in entries], 660 expand_composites=expand_composites) 661 ~\anaconda3\lib\site-packages\tensorflow\python\keras\engine\data_adapter.py in _convert_numpy_and_scipy(x) 1009 if issubclass(x.dtype.type, np.floating): 1010 dtype = backend.floatx() -> 1011 return ops.convert_to_tensor_v2_with_dispatch(x, dtype=dtype) 1012 elif scipy_sparse and scipy_sparse.issparse(x): 1013 return _scipy_sparse_to_sparse_tensor(x) ~\anaconda3\lib\site-packages\tensorflow\python\util\dispatch.py in wrapper(*args, **kwargs) 199 """Call target, and fall back on dispatchers if there is a TypeError.""" 200 try: --> 201 return target(*args, **kwargs) 202 except (TypeError, ValueError): 203 # Note: convert_to_eager_tensor currently raises a ValueError, not a ~\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in convert_to_tensor_v2_with_dispatch(value, dtype, dtype_hint, name) 1403 """ 1404 return convert_to_tensor_v2( -> 1405 value, dtype=dtype, dtype_hint=dtype_hint, name=name) 1406 1407 ~\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in convert_to_tensor_v2(value, dtype, dtype_hint, name) 1413 name=name, 1414 preferred_dtype=dtype_hint, -> 1415 as_ref=False) 1416 1417 ~\anaconda3\lib\site-packages\tensorflow\python\profiler\trace.py in wrapped(*args, **kwargs) 161 with Trace(trace_name, **trace_kwargs): 162 return func(*args, **kwargs) --> 163 return func(*args, **kwargs) 164 165 return wrapped ~\anaconda3\lib\site-packages\tensorflow\python\framework\ops.py in convert_to_tensor(value, dtype, name, as_ref, preferred_dtype, dtype_hint, ctx, accepted_result_types) 1538 1539 if ret is None: -> 1540 ret = conversion_func(value, dtype=dtype, name=name, as_ref=as_ref) 1541 1542 if ret is NotImplemented: ~\anaconda3\lib\site-packages\tensorflow\python\framework\tensor_conversion_registry.py in _default_conversion_function(***failed resolving arguments***) 50 def _default_conversion_function(value, dtype, name, as_ref): 51 del as_ref # Unused. ---> 52 return constant_op.constant(value, dtype, name=name) 53 54 ~\anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py in constant(value, dtype, shape, name) 263 """ 264 return _constant_impl(value, dtype, shape, name, verify_shape=False, --> 265 allow_broadcast=True) 266 267 ~\anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py in _constant_impl(value, dtype, shape, name, verify_shape, allow_broadcast) 274 with trace.Trace("tf.constant"): 275 return _constant_eager_impl(ctx, value, dtype, shape, verify_shape) --> 276 return _constant_eager_impl(ctx, value, dtype, shape, verify_shape) 277 278 g = ops.get_default_graph() ~\anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py in _constant_eager_impl(ctx, value, dtype, shape, verify_shape) 299 def _constant_eager_impl(ctx, value, dtype, shape, verify_shape): 300 """Implementation of eager constant.""" --> 301 t = convert_to_eager_tensor(value, ctx, dtype) 302 if shape is None: 303 return t ~\anaconda3\lib\site-packages\tensorflow\python\framework\constant_op.py in convert_to_eager_tensor(value, ctx, dtype) 96 dtype = dtypes.as_dtype(dtype).as_datatype_enum 97 ctx.ensure_initialized() ---> 98 return ops.EagerTensor(value, ctx.device_name, dtype) 99 100 ValueError: Failed to convert a NumPy array to a Tensor (Unsupported object type list).

أقوم بتدريب شبكة عصبونية للتعرف على الأرقام mnist ولكنها لا تعمل على google colab import numpy as np import pandas as pd from keras.models import Sequential from keras.layers import Dense , Activation, Dropout from keras.models import Sequential from keras.utils.np_utils import to_categorical from keras.datasets import mnist (x_train, y_train),(x_vaild, y_vaild) = mnist.load_data() y_train = to_categorical(y_train) y_vaild = to_categorical(y_vaild) image_size = x_train.shape[1] input_size = image_size * image_size x_train = np.reshape(x_train, [-1, input_size]) x_train = x_train.astype('float32') / 255 x_test = np.reshape(x_test, [-1, input_size]) x_test = x_test.astype('float32') / 255 batch_size = 32 hidden_units = 256 model = Sequential() model.add(Dense(hidden_units, input_dim=input_size)) model.add(Activation('relu')) model.add(Dropout(0,45)) model.add(Dense(hidden_units)) model.add(Activation('relu')) model.add(Dropout(0,45)) model.add(Dense(10)) model.add(Activation('softmax')) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(x_train, y_train, epochs=5,validation_data=[x_vaild, y_vaild], batch_size=batch_size)) --------------------------------------------------------------------------- TypeError Traceback (most recent call last) <ipython-input-10-51503a456d4c> in <module>() 34 model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) 35 ---> 36 model.fit(x_train, y_train, epochs=5,validation_data=[x_vaild, y_vaild], batch_size=batch_size) 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 TypeError: in user code: /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:830 train_function * return step_function(self, iterator) /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:813 run_step * outputs = model.train_step(data) /usr/local/lib/python3.7/dist-packages/keras/engine/training.py:770 train_step * y_pred = self(x, training=True) /usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:1006 __call__ * outputs = call_fn(inputs, *args, **kwargs) /usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:375 call * return super(Sequential, self).call(inputs, training=training, mask=mask) /usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:416 call * inputs, training=training, mask=mask) /usr/local/lib/python3.7/dist-packages/keras/engine/functional.py:551 _run_internal_graph * outputs = node.layer(*args, **kwargs) /usr/local/lib/python3.7/dist-packages/keras/engine/base_layer.py:1006 __call__ * outputs = call_fn(inputs, *args, **kwargs) /usr/local/lib/python3.7/dist-packages/keras/layers/core.py:205 dropped_inputs * inputs, /usr/local/lib/python3.7/dist-packages/keras/utils/control_flow_util.py:107 smart_cond * pred, true_fn=true_fn, false_fn=false_fn, name=name) /usr/local/lib/python3.7/dist-packages/keras/layers/core.py:195 _get_noise_shape * for i, value in enumerate(self.noise_shape): /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/operators/py_builtins.py:400 enumerate_ return _py_enumerate(s, start) /usr/local/lib/python3.7/dist-packages/tensorflow/python/autograph/operators/py_builtins.py:408 _py_enumerate return enumerate(s, start) TypeError: 'int' object is not iterable

ماهي ال KL-Divergence وكيف يمكن استخدامها في Kears؟

أحتاج طريقة لإنشاء مجموعة بيانات عشوائية لمهمة توقع في مكتبة Sklearn؟

كيف نقوم بتطبيق خوارزمية ExtraTreesRegressor في Sklearn؟

تطبيق التحويل PowerTransformer باستخدام مكتبة Sklearn؟

كيف نقوم بتطبيق خوارزمية AdaBoostRegressor في مكتبة Sklearn؟

قمت ببناء نموذج لتصنيف الصور مكون من عدة طبقات CNN و AveragePooling لكن لا أعلم سبب الخطأ التالي: # write your model here, we prefer that you call it model2 to make comparisions easier later: model2 = keras.Sequential() model2.add(layers.Conv2D(filters=6, kernel_size=(3, 3), activation='relu', input_shape=(150,150,3))) model2.add(layers.AveragePooling2D()) model2.add(layers.Conv2D(filters=16, kernel_size=(3, 3), activation='relu')) model2.add(layers.AveragePooling2D()) model2.add(layers.Dense(units=120, activation='tanh')) model2.add(layers.Dense(units=84, activation='tanh')) model2.add(layers.Dense(units=6, activation = 'softmax')) model2.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics=['accuracy']) model2.fit(train_images, train_labels, batch_size=32, epochs=5, validation_split = 0.2) --------------------------------------------------------------------------- InvalidArgumentError Traceback (most recent call last) <ipython-input-9-89da9186b401> in <module> 9 model2.add(layers.Dense(units=6, activation = 'softmax')) 10 model2.compile(optimizer = 'adam', loss = 'sparse_categorical_crossentropy', metrics=['accuracy']) ---> 11 model2.fit(train_images, train_labels, batch_size=32, epochs=5, 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) 886 # Lifting succeeded, so variables are initialized and we can run the 887 # stateless function. --> 888 return self._stateless_fn(*args, **kwds) 889 else: 890 _, _, _, filtered_flat_args = \ ~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in __call__(self, *args, **kwargs) 2941 filtered_flat_args) = self._maybe_define_function(args, kwargs) 2942 return graph_function._call_flat( -> 2943 filtered_flat_args, captured_inputs=graph_function.captured_inputs) # pylint: disable=protected-access 2944 2945 @property ~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in _call_flat(self, args, captured_inputs, cancellation_manager) 1917 # No tape is watching; skip to running the function. 1918 return self._build_call_outputs(self._inference_function.call( -> 1919 ctx, args, cancellation_manager=cancellation_manager)) 1920 forward_backward = self._select_forward_and_backward_functions( 1921 args, ~\anaconda3\lib\site-packages\tensorflow\python\eager\function.py in call(self, ctx, args, cancellation_manager) 558 inputs=args, 559 attrs=attrs, --> 560 ctx=ctx) 561 else: 562 outputs = execute.execute_with_cancellation( ~\anaconda3\lib\site-packages\tensorflow\python\eager\execute.py in quick_execute(op_name, num_outputs, inputs, attrs, ctx, name) 58 ctx.ensure_initialized() 59 tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name, ---> 60 inputs, attrs, num_outputs) 61 except core._NotOkStatusException as e: 62 if name is not None: InvalidArgumentError: logits and labels must have the same first dimension, got logits shape [41472,6] and labels shape [32] [[node sparse_categorical_crossentropy/SparseSoftmaxCrossEntropyWithLogits/SparseSoftmaxCrossEntropyWithLogits (defined at <ipython-input-9-89da9186b401>:11) ]] [Op:__inference_train_function_1875] Function call stack: train_function

أقوم بالعمل على موديل بسيط في keras ولكن لا أعرف ما الخطأ في keras.engine.Network: import keras from keras import backend from keras.models import Sequential from keras import layers from keras.datasets import mnist model = Sequential() model.add(layers.Flatten()) model.add(layers.Dense(512, activation='relu', input_shape=( 28*28,))) model.add(layers.Dense(10, activation='softmax')) model.compile(optimizer='Adam', loss='sparse_categorical_crossentropy', metrics=['accuracy']) def init(model): session = backend.get_session() for layer in model.layers: if isinstance(layer,keras.engine.Network): init(layer) init(model) --------------------------------------------------------------------------------------------- AttributeError Traceback (most recent call last) <ipython-input-9-487617649ac0> in <module>() 16 if isinstance(layer,keras.engine.Network): 17 init(layer) ---> 18 init(model) <ipython-input-9-487617649ac0> in init(model) 14 session = backend.get_session() 15 for layer in model.layers: ---> 16 if isinstance(layer,keras.engine.Network): 17 init(layer) 18 init(model) AttributeError: module 'keras.engine' has no attribute 'Network'

خوارزمية التحسين Adadelta Optimization واستخدامها في Keras؟

ال Poisson loss في Kears وكيف يتم استخدامها؟