Python keras.utils() Examples
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Example #1
Source File: cnn.py From DeepFashion with Apache License 2.0 | 7 votes |
def load_and_preprocess_data_3(): # The data, shuffled and split between train and test sets: (X_train, y_train), (x_test, y_test) = cifar10.load_data() logging.debug('X_train shape: {}'.format(X_train.shape)) logging.debug('train samples: {}'.format(X_train.shape[0])) logging.debug('test samples: {}'.format(x_test.shape[0])) # Convert class vectors to binary class matrices. y_train = keras.utils.to_categorical(y_train, num_classes) y_test = keras.utils.to_categorical(y_test, num_classes) X_train = X_train.astype('float32') x_test = x_test.astype('float32') X_train /= 255 x_test /= 255 input_shape = X_train[0].shape logging.debug('input_shape {}'.format(input_shape)) input_shape = X_train.shape[1:] logging.debug('input_shape {}'.format(input_shape)) return X_train, x_test, y_train, y_test, input_shape
Example #2
Source File: cnn_model.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 6 votes |
def replace_unknown_words(self, src_word_seq, trg_word_seq, hard_alignment, unk_symbol, heuristic=0, mapping=None, verbose=0): """ Replaces unknown words from the target sentence according to some heuristic. Borrowed from: https://github.com/sebastien-j/LV_groundhog/blob/master/experiments/nmt/replace_UNK.py :param src_word_seq: Source sentence words :param trg_word_seq: Hypothesis words :param hard_alignment: Target-Source alignments :param unk_symbol: Symbol in trg_word_seq to replace :param heuristic: Heuristic (0, 1, 2) :param mapping: External alignment dictionary :param verbose: Verbosity level :return: trg_word_seq with replaced unknown words """ print "WARNING!: deprecated function, use utils.replace_unknown_words() instead" return replace_unknown_words(src_word_seq, trg_word_seq, hard_alignment, unk_symbol, heuristic=heuristic, mapping=mapping, verbose=verbose)
Example #3
Source File: cnn_model-predictor.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 6 votes |
def replace_unknown_words(self, src_word_seq, trg_word_seq, hard_alignment, unk_symbol, heuristic=0, mapping=None, verbose=0): """ Replaces unknown words from the target sentence according to some heuristic. Borrowed from: https://github.com/sebastien-j/LV_groundhog/blob/master/experiments/nmt/replace_UNK.py :param src_word_seq: Source sentence words :param trg_word_seq: Hypothesis words :param hard_alignment: Target-Source alignments :param unk_symbol: Symbol in trg_word_seq to replace :param heuristic: Heuristic (0, 1, 2) :param mapping: External alignment dictionary :param verbose: Verbosity level :return: trg_word_seq with replaced unknown words """ print "WARNING!: deprecated function, use utils.replace_unknown_words() instead" return replace_unknown_words(src_word_seq, trg_word_seq, hard_alignment, unk_symbol, heuristic=heuristic, mapping=mapping, verbose=verbose)
Example #4
Source File: TransferLearning_reg.py From Intelligent-Projects-Using-Python with MIT License | 6 votes |
def __data_generation(self,list_files,labels): 'Generates data containing batch_size samples' # X : (n_samples, *dim, n_channels) # Initialization X = np.empty((len(list_files),self.dim[0],self.dim[1],self.dim[2])) y = np.empty((len(list_files)),dtype=int) # print(X.shape,y.shape) # Generate data k = -1 for i,f in enumerate(list_files): # print(f) img = get_im_cv2(f,dim=self.dim[0]) img = pre_process(img) label = labels[i] #label = keras.utils.np_utils.to_categorical(label,self.n_classes) X[i,] = img y[i,] = label # print(X.shape,y.shape) return X,y
Example #5
Source File: models.py From SeqGAN with MIT License | 6 votes |
def generate_samples(self, T, g_data, num, output_file): ''' Generate sample sentences to output file # Arguments: T: int, max time steps g_data: SeqGAN.utils.GeneratorPretrainingGenerator num: int, number of sentences output_file: str, path ''' sentences=[] for _ in range(num // self.B + 1): actions = self.sampling_sentence(T) actions_list = actions.tolist() for sentence_id in actions_list: sentence = [g_data.id2word[action] for action in sentence_id] sentences.append(sentence) output_str = '' for i in range(num): output_str += ' '.join(sentences[i]) + '\n' with open(output_file, 'w', encoding='utf-8') as f: f.write(output_str)
Example #6
Source File: deep_emotion_recognition.py From emotion-recognition-using-speech with MIT License | 6 votes |
def confusion_matrix(self, percentage=True, labeled=True): """Compute confusion matrix to evaluate the test accuracy of the classification""" if not self.classification: raise NotImplementedError("Confusion matrix works only when it is a classification problem") y_pred = self.model.predict_classes(self.X_test)[0] # invert from keras.utils.to_categorical y_test = np.array([ np.argmax(y, axis=None, out=None) for y in self.y_test[0] ]) matrix = confusion_matrix(y_test, y_pred, labels=[self.emotions2int[e] for e in self.emotions]).astype(np.float32) if percentage: for i in range(len(matrix)): matrix[i] = matrix[i] / np.sum(matrix[i]) # make it percentage matrix *= 100 if labeled: matrix = pd.DataFrame(matrix, index=[ f"true_{e}" for e in self.emotions ], columns=[ f"predicted_{e}" for e in self.emotions ]) return matrix
Example #7
Source File: __init__.py From EfficientDet with Apache License 2.0 | 5 votes |
def inject_tfkeras_modules(func): import tensorflow.keras as tfkeras @functools.wraps(func) def wrapper(*args, **kwargs): kwargs['backend'] = tfkeras.backend kwargs['layers'] = tfkeras.layers kwargs['models'] = tfkeras.models kwargs['utils'] = tfkeras.utils return func(*args, **kwargs) return wrapper
Example #8
Source File: __init__.py From garbage_classify with Apache License 2.0 | 5 votes |
def get_submodules_from_kwargs(kwargs): backend = keras.backend layers = keras.backend models = keras.models keras_utils = keras.utils return backend, layers, models, keras_utils
Example #9
Source File: __init__.py From garbage_classify with Apache License 2.0 | 5 votes |
def inject_keras_modules(func): import keras @functools.wraps(func) def wrapper(*args, **kwargs): kwargs['backend'] = keras.backend kwargs['layers'] = keras.layers kwargs['models'] = keras.models kwargs['utils'] = keras.utils return func(*args, **kwargs) return wrapper
Example #10
Source File: __init__.py From segmentation_models with MIT License | 5 votes |
def get_preprocessing(name): preprocess_input = Backbones.get_preprocessing(name) # add bakcend, models, layers, utils submodules in kwargs preprocess_input = inject_global_submodules(preprocess_input) # delete other kwargs # keras-applications preprocessing raise an error if something # except `backend`, `layers`, `models`, `utils` passed in kwargs preprocess_input = filter_kwargs(preprocess_input) return preprocess_input
Example #11
Source File: __init__.py From segmentation_models with MIT License | 5 votes |
def filter_kwargs(func): @functools.wraps(func) def wrapper(*args, **kwargs): new_kwargs = {k: v for k, v in kwargs.items() if k in ['backend', 'layers', 'models', 'utils']} return func(*args, **new_kwargs) return wrapper
Example #12
Source File: __init__.py From segmentation_models with MIT License | 5 votes |
def inject_global_submodules(func): @functools.wraps(func) def wrapper(*args, **kwargs): kwargs['backend'] = _KERAS_BACKEND kwargs['layers'] = _KERAS_LAYERS kwargs['models'] = _KERAS_MODELS kwargs['utils'] = _KERAS_UTILS return func(*args, **kwargs) return wrapper
Example #13
Source File: __init__.py From garbage_classify with Apache License 2.0 | 5 votes |
def init_tfkeras_custom_objects(): import tensorflow.keras as tfkeras from . import model custom_objects = { 'swish': inject_tfkeras_modules(model.get_swish)(), 'FixedDropout': inject_tfkeras_modules(model.get_dropout)() } tfkeras.utils.get_custom_objects().update(custom_objects)
Example #14
Source File: __init__.py From garbage_classify with Apache License 2.0 | 5 votes |
def inject_tfkeras_modules(func): import tensorflow.keras as tfkeras @functools.wraps(func) def wrapper(*args, **kwargs): kwargs['backend'] = tfkeras.backend kwargs['layers'] = tfkeras.layers kwargs['models'] = tfkeras.models kwargs['utils'] = tfkeras.utils return func(*args, **kwargs) return wrapper
Example #15
Source File: data_gen_label.py From garbage_classify with Apache License 2.0 | 5 votes |
def get_submodules_from_kwargs(kwargs): backend = keras.backend layers = keras.backend models = keras.models keras_utils = keras.utils return backend, layers, models, keras_utils
Example #16
Source File: __init__.py From EfficientDet with Apache License 2.0 | 5 votes |
def init_tfkeras_custom_objects(): import tensorflow.keras as tfkeras import efficientnet as model custom_objects = { 'swish': inject_tfkeras_modules(model.get_swish)(), 'FixedDropout': inject_tfkeras_modules(model.get_dropout)() } tfkeras.utils.get_custom_objects().update(custom_objects)
Example #17
Source File: ast_attendgru_xtra.py From funcom with GNU General Public License v3.0 | 5 votes |
def create_model(self): dat_input = Input(shape=(self.tdatlen,)) com_input = Input(shape=(self.comlen,)) sml_input = Input(shape=(self.smllen,)) ee = Embedding(output_dim=self.embdims, input_dim=self.tdatvocabsize, mask_zero=False)(dat_input) se = Embedding(output_dim=self.smldims, input_dim=self.smlvocabsize, mask_zero=False)(sml_input) se_enc = CuDNNGRU(self.recdims, return_state=True, return_sequences=True) seout, state_sml = se_enc(se) enc = CuDNNGRU(self.recdims, return_state=True, return_sequences=True) encout, state_h = enc(ee, initial_state=state_sml) de = Embedding(output_dim=self.embdims, input_dim=self.comvocabsize, mask_zero=False)(com_input) dec = CuDNNGRU(self.recdims, return_sequences=True) decout = dec(de, initial_state=state_h) attn = dot([decout, encout], axes=[2, 2]) attn = Activation('softmax')(attn) context = dot([attn, encout], axes=[2, 1]) ast_attn = dot([decout, seout], axes=[2, 2]) ast_attn = Activation('softmax')(ast_attn) ast_context = dot([ast_attn, seout], axes=[2, 1]) context = concatenate([context, decout, ast_context]) out = TimeDistributed(Dense(self.recdims, activation="relu"))(context) out = Flatten()(out) out = Dense(self.comvocabsize, activation="softmax")(out) model = Model(inputs=[dat_input, com_input, sml_input], outputs=out) if self.config['multigpu']: model = keras.utils.multi_gpu_model(model, gpus=2) model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy']) return self.config, model
Example #18
Source File: test_build.py From keras-efficientnets with MIT License | 5 votes |
def get_preds(model): size = model.input_shape[1] filename = os.path.join(os.path.dirname(__file__), 'data', '565727409_61693c5e14.jpg') batch = KE.preprocess_input(img_to_array(load_img( filename, target_size=(size, size)))) batch = np.expand_dims(batch, 0) pred = decode_predictions(model.predict(batch), backend=K, utils=utils) return pred
Example #19
Source File: __init__.py From EfficientDet with Apache License 2.0 | 5 votes |
def inject_keras_modules(func): import keras @functools.wraps(func) def wrapper(*args, **kwargs): kwargs['backend'] = keras.backend kwargs['layers'] = keras.layers kwargs['models'] = keras.models kwargs['utils'] = keras.utils return func(*args, **kwargs) return wrapper
Example #20
Source File: __init__.py From EfficientDet with Apache License 2.0 | 5 votes |
def get_submodules_from_kwargs(kwargs): backend = kwargs.get('backend', _KERAS_BACKEND) layers = kwargs.get('layers', _KERAS_LAYERS) models = kwargs.get('models', _KERAS_MODELS) utils = kwargs.get('utils', _KERAS_UTILS) for key in kwargs.keys(): if key not in ['backend', 'layers', 'models', 'utils']: raise TypeError('Invalid keyword argument: %s', key) return backend, layers, models, utils
Example #21
Source File: cnn_model.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 5 votes |
def decode_predictions_one_hot(self, preds, index2word, verbose=0): """ Decodes predictions following a one-hot codification. :param preds: Predictions codified as one-hot vectors. :param index2word: Mapping from word indices into word characters. :param verbose: Verbosity level, by default 0. :return: List of decoded predictions """ print "WARNING!: deprecated function, use utils.decode_predictions_one_hot() instead" return decode_predictions_one_hot(preds, index2word, verbose=verbose)
Example #22
Source File: cnn_model.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 5 votes |
def one_hot_2_indices(self, preds, pad_sequences=True, verbose=0): """ Converts a one-hot codification into a index-based one :param preds: Predictions codified as one-hot vectors. :param verbose: Verbosity level, by default 0. :return: List of convertedpredictions """ print "WARNING!: deprecated function, use utils.one_hot_2_indices() instead" return one_hot_2_indices(preds, pad_sequences=pad_sequences, verbose=verbose)
Example #23
Source File: cnn_model.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 5 votes |
def decode_predictions(self, preds, temperature, index2word, sampling_type, verbose=0): """ Decodes predictions :param preds: Predictions codified as the output of a softmax activation function. :param temperature: Temperature for sampling. :param index2word: Mapping from word indices into word characters. :param sampling_type: 'max_likelihood' or 'multinomial'. :param verbose: Verbosity level, by default 0. :return: List of decoded predictions. """ print "WARNING!: deprecated function, use utils.decode_predictions() instead" return decode_predictions(preds, temperature, index2word, sampling_type, verbose=verbose)
Example #24
Source File: cnn_model.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 5 votes |
def sampling(self, scores, sampling_type='max_likelihood', temperature=1.0): """ Sampling words (each sample is drawn from a categorical distribution). Or picks up words that maximize the likelihood. :param scores: array of size #samples x #classes; every entry determines a score for sample i having class j :param sampling_type: :param temperature: Temperature for the predictions. The higher, the flatter probabilities. Hence more random outputs. :return: set of indices chosen as output, a vector of size #samples """ print "WARNING!: deprecated function, use utils.sampling() instead" return sampling(scores, sampling_type=sampling_type, temperature=temperature)
Example #25
Source File: cnn_model.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 5 votes |
def sample(self, a, temperature=1.0): """ Helper function to sample an index from a probability array :param a: Probability array :param temperature: The higher, the flatter probabilities. Hence more random outputs. :return: """ print "WARNING!: deprecated function, use utils.sample() instead" return sample(a, temperature=temperature)
Example #26
Source File: cnn_model-predictor.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 5 votes |
def decode_predictions_one_hot(self, preds, index2word, verbose=0): """ Decodes predictions following a one-hot codification. :param preds: Predictions codified as one-hot vectors. :param index2word: Mapping from word indices into word characters. :param verbose: Verbosity level, by default 0. :return: List of decoded predictions """ print "WARNING!: deprecated function, use utils.decode_predictions_one_hot() instead" return decode_predictions_one_hot(preds, index2word, verbose=verbose)
Example #27
Source File: cnn_model-predictor.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 5 votes |
def decode_predictions_beam_search(self, preds, index2word, alphas=None, heuristic=0, x_text=None, unk_symbol='<unk>', pad_sequences=False, mapping=None, verbose=0): """ Decodes predictions from the BeamSearch method. :param preds: Predictions codified as word indices. :param index2word: Mapping from word indices into word characters. :param pad_sequences: Whether we should make a zero-pad on the input sequence. :param verbose: Verbosity level, by default 0. :return: List of decoded predictions """ print "WARNING!: deprecated function, use utils.decode_predictions_beam_search() instead" return decode_predictions_beam_search(preds, index2word, alphas=alphas, heuristic=heuristic, x_text=x_text, unk_symbol=unk_symbol, pad_sequences=pad_sequences, mapping=mapping, verbose=0)
Example #28
Source File: utils_mnist.py From robust_physical_perturbations with MIT License | 5 votes |
def model_mnist(logits=False, input_ph=None, img_rows=28, img_cols=28, nb_filters=64, nb_classes=10): warnings.warn("`utils_mnist.model_mnist` is deprecated. Switch to" "`utils.cnn_model`. `utils_mnist.model_mnist` will " "be removed after 2017-08-17.") return utils.cnn_model(logits=logits, input_ph=input_ph, img_rows=img_rows, img_cols=img_cols, nb_filters=nb_filters, nb_classes=nb_classes)
Example #29
Source File: cnn_model-predictor.py From deepQuest with BSD 3-Clause "New" or "Revised" License | 5 votes |
def decode_predictions(self, preds, temperature, index2word, sampling_type, verbose=0): """ Decodes predictions :param preds: Predictions codified as the output of a softmax activation function. :param temperature: Temperature for sampling. :param index2word: Mapping from word indices into word characters. :param sampling_type: 'max_likelihood' or 'multinomial'. :param verbose: Verbosity level, by default 0. :return: List of decoded predictions. """ print "WARNING!: deprecated function, use utils.decode_predictions() instead" return decode_predictions(preds, temperature, index2word, sampling_type, verbose=verbose)
Example #30
Source File: __init__.py From EfficientDet with Apache License 2.0 | 5 votes |
def init_keras_custom_objects(): import keras import efficientnet as model custom_objects = { 'swish': inject_keras_modules(model.get_swish)(), 'FixedDropout': inject_keras_modules(model.get_dropout)() } keras.utils.generic_utils.get_custom_objects().update(custom_objects)