我们从Python开源项目中，提取了以下17个代码示例，用于说明如何使用model.create_model()。
def main(_): model, argv = model_lib.create_model() run(model, argv)
def load_inference(sess, ckptdir, threshold): images = tf.placeholder(tf.float32, shape=[None, IMG_SIZE, IMG_SIZE, 3]) net = create_model(images, .1) sess.run(tf.global_variables_initializer()) saver = tf.train.Saver(max_to_keep=10) if ckptdir and os.path.exists(ckptdir) and not FLAGS.debug: checkpoint = tf.train.latest_checkpoint(ckptdir) if checkpoint: print('Restoring', checkpoint) saver.restore(sess, checkpoint) return inference(net, threshold), images ########### # Helpers # ###########
def predict(image_name, data_dir="/home/shagun/projects/Image-Caption-Generator/data/", weights_path=None, mode="test"): '''Method to predict the caption for a given image. weights_path is the path to the .h5 file (model)''' image_path = data_dir + "images/" + image_name vgg_model = load_vgg16() vgg_embedding = vgg_model.predict( load_image(image_path) ) image_embeddings = [vgg_embedding] config_dict = generate_config(data_dir=data_dir, mode=mode) print(config_dict) model = create_model(config_dict=config_dict, compile_model=False) model.load_weights(data_dir + "model/" + weights_path) tokenizer = get_tokenizer(config_dict=config_dict, data_dir=data_dir) index_to_word = {v: k for k, v in tokenizer.word_index.items()} for image_embedding in image_embeddings: gen_captions(config=config_dict, model=model, image_embedding=image_embedding, tokenizer=tokenizer, num_captions=2, index_to_word=index_to_word )
def perform_testing(settings, id_to_word_dict, captions_data=None, coco_images_dir=None, coco_num_images=None, coco_out_path=None): from preprocess import TokenBegin, TokenEnd with h5py.File(settings.preprocessed_images_file, 'r') as h5_images_file: image_shape = h5_images_file['images'].shape[1:] with h5py.File(settings.preprocessed_text_file, 'r') as h5_text_file: sentence_max_len = len(h5_text_file['sentences'][0]) dict_size = len(id_to_word_dict) TokenBeginIndex = find_token_index(id_to_word_dict, TokenBegin) TokenEndIndex = find_token_index(id_to_word_dict, TokenEnd) model = create_model(image_shape, dict_size, sentence_max_len, settings) model.load_weights(settings.weights) if captions_data is not None and coco_images_dir is not None and coco_num_images is not None \ and coco_out_path is not None: calculate_metrics(settings, model, id_to_word_dict, captions_data, coco_images_dir, coco_num_images, coco_out_path, image_shape[:2], sentence_max_len, TokenBeginIndex, TokenEndIndex) else: create_caption_for_path(settings.test_source, model, image_shape[:2], sentence_max_len, TokenBeginIndex, TokenEndIndex, id_to_word_dict, settings.output_dir, (settings.out_max_width, settings.out_max_height))
def train(batch_size=128, epochs=100, data_dir="/home/shagun/projects/Image-Caption-Generator/data/", weights_path=None, mode="train"): '''Method to train the image caption generator weights_path is the path to the .h5 file where weights from the previous run are saved (if available)''' config_dict = generate_config(data_dir=data_dir, mode=mode) config_dict['batch_size'] = batch_size steps_per_epoch = config_dict["total_number_of_examples"] // batch_size print("steps_per_epoch = ", steps_per_epoch) train_data_generator = debug_generator(config_dict=config_dict, data_dir=data_dir) model = create_model(config_dict=config_dict) if weights_path: model.load_weights(weights_path) file_name = data_dir + "model/weights-{epoch:02d}.hdf5" checkpoint = ModelCheckpoint(filepath=file_name, monitor='loss', verbose=1, save_best_only=True, mode='min') tensorboard = TensorBoard(log_dir='../logs', histogram_freq=0, batch_size=batch_size, write_graph=True, write_grads=True, write_images=False, embeddings_freq=0, embeddings_layer_names=None, embeddings_metadata=None) callbacks_list = [checkpoint, tensorboard] model.fit_generator( generator=train_data_generator, steps_per_epoch=steps_per_epoch, epochs=epochs, verbose=2, callbacks=callbacks_list)
def main(): parser = argparse.ArgumentParser(description='Predicts pixel intensities given a random subset of an image.') # Experiment settings parser.add_argument('--inputdir', default='experiments/pixels/data', help='The directory where the input data files will be stored.') parser.add_argument('--outputdir', default='experiments/pixels/results', help='The directory where the input data files will be stored.') parser.add_argument('--variable_scope', default='pixels-', help='The variable scope that the model will be created with.') parser.add_argument('--train_id', type=int, default=0, help='A trial ID. All models trained with the same trial ID will use the same train/validation datasets.') parser.add_argument('--train_samples', type=int, default=50000, help='The number of training examples to use.') parser.add_argument('--test_samples', type=int, default=10000, help='The number of training examples to use.') parser.add_argument('--validation_pct', type=float, default=0.2, help='The number of samples to hold out for a validation set. This is a percentage of the training samples.') parser.add_argument('--dimsize', type=int, default=256, help='The number of bins for each subpixel intensity (max 256, must be a power of 2).') parser.add_argument('--batchsize', type=int, default=50, help='The number of training samples per mini-batch.') # GMM/LMM settings parser.add_argument('--num_components', type=int, default=5, help='The number of mixture components for gmm or lmm models.') # Get the arguments from the command line args = parser.parse_args() dargs = vars(args) dargs['model'] = 'gmm' dargs['dataset'] = 'cifar' dargs['outfile'] = os.path.join(dargs['outputdir'], '{model}_{dataset}_{train_samples}_{num_components}_{train_id}'.format(**dargs)) dargs['variable_scope'] = '{model}-{dataset}-{train_samples}-{num_components}-{train_id}'.format(**dargs) # Get the data from cifar_utils import DataLoader train_data = DataLoader(args.inputdir, 'train', args.train_samples, args.batchsize, seed=args.train_id, dimsize=args.dimsize) validate_data = DataLoader(args.inputdir, 'validate', args.train_samples, args.batchsize, seed=args.train_id, dimsize=args.dimsize) test_data = DataLoader(args.inputdir, 'test', args.test_samples, args.batchsize, seed=args.train_id, dimsize=args.dimsize) dargs['x_shape'] = train_data.x_shape() dargs['y_shape'] = train_data.y_shape() dargs['lazy_density'] = True # density is too big to enumerate for cifar dargs['one_hot'] = False # We use just the intensities not a one-hot sess = tf.Session(config=tf.ConfigProto(log_device_placement=False)) # Get the X placeholder and the output distribution model tf_X, dist = create_model(**dargs) saver = tf.train.Saver() sess.run(tf.global_variables_initializer()) # Reset the model back to the best version saver.restore(sess, dargs['outfile']) logprobs, rmse = explicit_score(sess, args.model, dist, test_data, tf_X) print logprobs, rmse np.savetxt(dargs['outfile'] + '_score.csv', [best_loss, logprobs, rmse, args.k, args.lam, args.num_components])
def train_model_CV(slices_images,slices_labels,slice_number,f): ''' Training model using cross-validation passing the slices manually Parameters ---------- slices_images: list of numpy.array slices_labels: list of numpy.array Output ---------- Write in a results file the mean of the accuracy in the test set ''' #images,labels,list_of_images = reorderRandomly(images,labels,list_of_images) ''' for i in range(len(labels)): if labels[i] == "AD": labels[i] = 0 else: labels[i] = 1 slices_images = [images[i::5] for i in range(5)] slices_list_of_images = [list_of_images[i::5] for i in range(5)] slices_labels = [labels[i::5] for i in range(5)] print(slices_list_of_images) ''' values_acc = [] for i in range(5): model = create_model() X_test = slices_images[i] Y_test = slices_labels[i] X_train = [item for s in slices_images if s is not X_test for item in s] Y_train = [item for s in slices_labels if s is not Y_test for item in s] X_train = np.array(X_train) Y_train = np.array(Y_train) X_test = np.array(X_test) Y_test = np.array(Y_test) from keras.utils.np_utils import to_categorical Y_train = to_categorical(Y_train) Y_test = to_categorical(Y_test) history = model.fit(X_train,Y_train,epochs=70,batch_size=5,verbose=0) test_loss = model.evaluate(X_test,Y_test) print("Loss and accuracy in the test set: Loss %g, Accuracy %g"%(test_loss[0],test_loss[1])) values_acc.append(test_loss[1]) mean = calculate_mean(values_acc) f.write(("The mean of all the test values for the slice %g is: %g \n"%(slice_number,mean)))
def train_model_CV_generator(images,labels,model,train_datagen): ''' Training model using cross-validation Parameters ---------- ''' train_datagen = ImageDataGenerator(horizontal_flip=True) images,labels = reorderRandomly(images,labels) for i in range(len(labels)): if labels[i] == "AD": labels[i] = 0 else: labels[i] = 1 slices_images = [images[i::5] for i in range(5)] slices_labels = [labels[i::5] for i in range(5)] models = {} histories = {} values_acc = [] for i in range(5): model = create_model() X_test = slices_images[i] Y_test = slices_labels[i] X_train = [item for s in slices_images if s is not X_test for item in s] Y_train = [item for s in slices_labels if s is not Y_test for item in s] X_train = np.array(X_train) Y_train = np.array(Y_train) X_test = np.array(X_test) Y_test = np.array(Y_test) from keras.utils.np_utils import to_categorical Y_train = to_categorical(Y_train) Y_test = to_categorical(Y_test) history = model.fit_generator(train_datagen.flow(X_train,Y_train,batch_size = 5), epochs=70,steps_per_epoch= len(X_train)//5) models['model'+str(i)] = model test_loss = model.evaluate(X_test,Y_test) print("Loss and accuracy in the test set: Loss %g, Accuracy %g"%(test_loss[0],test_loss[1])) histories['test_acc'+str(i)] = test_loss values_acc.append(test_loss[1]) mean = calculate_mean(values_acc) print("The mean of all the test values is: %g"%mean)
def train_LOO(images,labels): ''' Training model using LOO validation using all the images Parameters ---------- images: list of numpy.array labels: list of numpy.array Output ---------- Print the mean of the LOO validation ''' values_acc = [] for i in range(len(labels)): if labels[i] == "AD": labels[i] = 0 else: labels[i] = 1 print("The lenght of images is "+str(len(images))) for i in range(len(images)): print("We are in the fold "+str(i)) model = create_model() X_test = [] Y_test = [] X_test.append(images[i]) Y_test.append(labels[i]) X_train = [] Y_train = [] for j in range(len(images)): if j != i: X_train.append(images[j]) Y_train.append(labels[j]) X_train = np.array(X_train) Y_train = np.array(Y_train) X_test = np.array(X_test) Y_test = np.array(Y_test) from keras.utils.np_utils import to_categorical Y_train = to_categorical(Y_train,2) Y_test = to_categorical(Y_test,2) history = model.fit(X_train,Y_train,epochs=70,batch_size=10,verbose=0) test_loss = model.evaluate(X_test,Y_test) print("Loss and accuracy in the test set: Loss %g, Accuracy %g"%(test_loss[0],test_loss[1])) values_acc.append(test_loss[1]) mean = calculate_mean(values_acc) print("The mean of all the test values is: %g"%mean)
def train_LOO_pacient(images,labels,names): ''' Training model using LOO validation by pacient Parameters ---------- images: list of numpy.array. images of the pacients labels: list of numpy.array. labels of the different images names: list of numpy.array. name of the pacients Output ---------- Print the mean of the LOO validation ''' print("Training LOO with pacient name") values_acc = [] already_tested = [] for i in range(len(labels)): if labels[i] == "AD": labels[i] = 0 else: labels[i] = 1 print("The lenght of images is "+str(len(images))) for i in range(len(images)): model = create_model() X_test = [] Y_test = [] # If we haven't tested that pacient already # we insert it to the test set and also # all the images with the same name if(not(names[i] in already_tested)): already_tested.append(names[i]) X_test.append(images[i]) Y_test.append(labels[i]) for j in range(len(images)): if j!=i and names[j] == names[i]: X_test.append(images[j]) Y_test.append(labels[j]) X_train = [] Y_train = [] for j in range(len(images)): if names[j] != names[i]: X_train.append(images[j]) Y_train.append(labels[j]) X_train = np.array(X_train) Y_train = np.array(Y_train) X_test = np.array(X_test) Y_test = np.array(Y_test) from keras.utils.np_utils import to_categorical Y_train = to_categorical(Y_train,2) Y_test = to_categorical(Y_test,2) print("The len of the training set is: "+str(len(X_train))) print("The len of the test set is: "+str(len(X_test))) history = model.fit(X_train,Y_train,epochs=70,batch_size=10) test_loss = model.evaluate(X_test,Y_test) print("Loss and accuracy in the test set: Loss %g, Accuracy %g"%(test_loss[0],test_loss[1])) values_acc.append(test_loss[1]) mean = calculate_mean(values_acc) print("The mean of all the test values is: %g"%mean)
