Python keras.utils() Examples
The following are 30 code examples for showing how to use keras.utils(). These examples are extracted from open source projects. You can vote up the ones you like or vote down the ones you don't like, and go to the original project or source file by following the links above each example.
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Example 1
Project: DeepFashion Author: abhishekrana File: cnn.py License: Apache License 2.0 | 6 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
Project: SeqGAN Author: tyo-yo File: models.py License: 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 3
Project: Intelligent-Projects-Using-Python Author: PacktPublishing File: TransferLearning_reg.py License: 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 4
Project: deepQuest Author: sheffieldnlp File: cnn_model-predictor.py License: 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 5
Project: deepQuest Author: sheffieldnlp File: cnn_model.py License: 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 6
Project: emotion-recognition-using-speech Author: x4nth055 File: deep_emotion_recognition.py License: 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
Project: robust_physical_perturbations Author: evtimovi File: utils_mnist.py License: 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 8
Project: robust_physical_perturbations Author: evtimovi File: utils_mnist.py License: 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 9
Project: keras-efficientnets Author: titu1994 File: test_build.py License: 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 10
Project: Intelligent-Projects-Using-Python Author: PacktPublishing File: captcha_solver.py License: MIT License | 5 votes |
def train(dest_train,dest_val,outdir,batch_size,n_classes,dim,shuffle,epochs,lr): char_to_index_dict,index_to_char_dict = create_dict_char_to_index() model = _model_(n_classes) from keras.utils import plot_model plot_model(model, to_file=outdir + 'model.png') train_generator = DataGenerator(dest_train,char_to_index_dict,batch_size,n_classes,dim,shuffle) val_generator = DataGenerator(dest_val,char_to_index_dict,batch_size,n_classes,dim,shuffle) model.fit_generator(train_generator,epochs=epochs,validation_data=val_generator) model.save(outdir + 'captcha_breaker.h5')
Example 11
Project: Intelligent-Projects-Using-Python Author: PacktPublishing File: TransferLearning.py License: MIT License | 5 votes |
def read_data(self,class_folders,path,num_class,dim,train_val='train'): print(train_val) train_X,train_y = [],[] for c in class_folders: path_class = path + str(train_val) + '/' + str(c) file_list = os.listdir(path_class) for f in file_list: img = self.get_im_cv2(path_class + '/' + f) img = self.pre_process(img) train_X.append(img) label = int(c.split('class')[1]) train_y.append(int(label)) train_y = keras.utils.np_utils.to_categorical(np.array(train_y),num_class) return np.array(train_X),train_y
Example 12
Project: voxelmorph Author: voxelmorph File: generators.py License: GNU General Public License v3.0 | 5 votes |
def _to_categorical(y, num_classes=None, reshape=True): """ # copy of keras.utils.np_utils.to_categorical, but with a boolean matrix instead of float Converts a class vector (integers) to binary class matrix. E.g. for use with categorical_crossentropy. # Arguments y: class vector to be converted into a matrix (integers from 0 to num_classes). num_classes: total number of classes. # Returns A binary matrix representation of the input. """ oshape = y.shape y = np.array(y, dtype='int').ravel() if not num_classes: num_classes = np.max(y) + 1 n = y.shape[0] categorical = np.zeros((n, num_classes), bool) categorical[np.arange(n), y] = 1 if reshape: categorical = np.reshape(categorical, [*oshape, num_classes]) return categorical
Example 13
Project: classification_models Author: qubvel File: keras.py License: MIT License | 5 votes |
def get_kwargs(): return { 'backend': keras.backend, 'layers': keras.layers, 'models': keras.models, 'utils': keras.utils, }
Example 14
Project: kaggle-rsna18 Author: i-pan File: train_kaggle.py License: MIT License | 5 votes |
def create_models(backbone_retinanet, num_classes, weights, multi_gpu=0, freeze_backbone=False): """ Creates three models (model, training_model, prediction_model). Args backbone_retinanet : A function to call to create a retinanet model with a given backbone. num_classes : The number of classes to train. weights : The weights to load into the model. multi_gpu : The number of GPUs to use for training. freeze_backbone : If True, disables learning for the backbone. Returns model : The base model. This is also the model that is saved in snapshots. training_model : The training model. If multi_gpu=0, this is identical to model. prediction_model : The model wrapped with utility functions to perform object detection (applies regression values and performs NMS). """ modifier = freeze_model if freeze_backbone else None # Keras recommends initialising a multi-gpu model on the CPU to ease weight sharing, and to prevent OOM errors. # optionally wrap in a parallel model if multi_gpu > 1: from keras.utils import multi_gpu_model with tf.device('/cpu:0'): model = model_with_weights(backbone_retinanet(num_classes, modifier=modifier), weights=weights, skip_mismatch=True) training_model = multi_gpu_model(model, gpus=multi_gpu) else: model = model_with_weights(backbone_retinanet(num_classes, modifier=modifier), weights=weights, skip_mismatch=True) training_model = model # make prediction model prediction_model = retinanet_bbox(model=model) # compile model training_model.compile( loss={ 'regression' : losses.smooth_l1(), 'classification': losses.focal() }, optimizer=keras.optimizers.adam(lr=1e-5, clipnorm=0.001) ) return model, training_model, prediction_model
Example 15
Project: kaggle-rsna18 Author: i-pan File: train.py License: MIT License | 5 votes |
def create_models(backbone_retinanet, num_classes, weights, multi_gpu=0, freeze_backbone=False): """ Creates three models (model, training_model, prediction_model). Args backbone_retinanet : A function to call to create a retinanet model with a given backbone. num_classes : The number of classes to train. weights : The weights to load into the model. multi_gpu : The number of GPUs to use for training. freeze_backbone : If True, disables learning for the backbone. Returns model : The base model. This is also the model that is saved in snapshots. training_model : The training model. If multi_gpu=0, this is identical to model. prediction_model : The model wrapped with utility functions to perform object detection (applies regression values and performs NMS). """ modifier = freeze_model if freeze_backbone else None # Keras recommends initialising a multi-gpu model on the CPU to ease weight sharing, and to prevent OOM errors. # optionally wrap in a parallel model if multi_gpu > 1: from keras.utils import multi_gpu_model with tf.device('/cpu:0'): model = model_with_weights(backbone_retinanet(num_classes, modifier=modifier), weights=weights, skip_mismatch=True) training_model = multi_gpu_model(model, gpus=multi_gpu) else: model = model_with_weights(backbone_retinanet(num_classes, modifier=modifier), weights=weights, skip_mismatch=True) training_model = model # make prediction model prediction_model = retinanet_bbox(model=model) # compile model training_model.compile( loss={ 'regression' : losses.smooth_l1(), 'classification': losses.focal() }, optimizer=keras.optimizers.adam(lr=1e-5, clipnorm=0.001) ) return model, training_model, prediction_model
Example 16
Project: keras-metrics Author: netrack File: test_metrics.py License: MIT License | 5 votes |
def create_categorical_samples(self, n): x, y = self.create_binary_samples(n) return x, keras.utils.to_categorical(y)
Example 17
Project: keras-metrics Author: netrack File: test_average_recall.py License: MIT License | 5 votes |
def create_samples(self, n, labels=1): x = numpy.random.uniform(0, numpy.pi/2, (n, labels)) y = numpy.random.randint(labels, size=(n, 1)) return x, keras.utils.to_categorical(y)
Example 18
Project: talos Author: autonomio File: datasets.py License: MIT License | 5 votes |
def iris(): import pandas as pd from keras.utils import to_categorical base = 'https://raw.githubusercontent.com/autonomio/datasets/master/autonomio-datasets/' df = pd.read_csv(base + 'iris.csv') df['species'] = df['species'].factorize()[0] df = df.sample(len(df)) y = to_categorical(df['species']) x = df.iloc[:, :-1].values y = to_categorical(df['species']) x = df.iloc[:, :-1].values return x, y
Example 19
Project: talos Author: autonomio File: datasets.py License: MIT License | 5 votes |
def mnist(): '''Note that this dataset, unlike other Talos datasets,returns: x_train, y_train, x_val, y_val''' import keras import numpy as np # the data, split between train and test sets (x_train, y_train), (x_val, y_val) = keras.datasets.mnist.load_data() # input image dimensions img_rows, img_cols = 28, 28 if keras.backend.image_data_format() == 'channels_first': x_train = x_train.reshape(x_train.shape[0], 1, img_rows, img_cols) x_val = x_val.reshape(x_val.shape[0], 1, img_rows, img_cols) input_shape = (1, img_rows, img_cols) else: x_train = x_train.reshape(x_train.shape[0], img_rows, img_cols, 1) x_val = x_val.reshape(x_val.shape[0], img_rows, img_cols, 1) input_shape = (img_rows, img_cols, 1) x_train = x_train.astype('float32') x_val = x_val.astype('float32') x_train /= 255 x_val /= 255 classes = len(np.unique(y_train)) # convert class vectors to binary class matrices y_train = keras.utils.to_categorical(y_train, classes) y_val = keras.utils.to_categorical(y_val, classes) print("Use input_shape %s" % str(input_shape)) return x_train, y_train, x_val, y_val
Example 20
Project: efficientnet Author: qubvel File: __init__.py License: 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
Project: efficientnet Author: qubvel File: __init__.py License: 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 22
Project: efficientnet Author: qubvel File: __init__.py License: 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 23
Project: efficientnet Author: qubvel File: __init__.py License: Apache License 2.0 | 5 votes |
def init_keras_custom_objects(): import keras from . import 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)
Example 24
Project: efficientnet Author: qubvel File: __init__.py License: 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 25
Project: deepQuest Author: sheffieldnlp File: cnn_model-predictor.py License: 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
Project: deepQuest Author: sheffieldnlp File: cnn_model-predictor.py License: 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 27
Project: deepQuest Author: sheffieldnlp File: cnn_model-predictor.py License: 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 28
Project: deepQuest Author: sheffieldnlp File: cnn_model-predictor.py License: 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 29
Project: deepQuest Author: sheffieldnlp File: cnn_model-predictor.py License: 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 30
Project: deepQuest Author: sheffieldnlp File: cnn_model.py License: 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)