def image_classifier_xception(conf, input, **kw): # extract conf f = conf['fit']['args'] e = conf['evaluate']['args'] epochs = f['epochs'] batch_size = f['batch_size'] # extract kw result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) result_dir = kw.pop('result_dir', None) # extract input train_data_dir = input['train_data_dir'] validation_data_dir = input['validation_data_dir'] nb_train_samples = input['nb_train_samples'] nb_validation_samples = input['nb_validation_samples'] # dimensions of our images. # use 150, 150 as default img_width, img_height = 150, 150 if K.image_data_format() == 'channels_first': input_shape = (3, img_width, img_height) else: input_shape = (img_width, img_height, 3) with graph.as_default(): return model_main(result_sds, project_id, result_dir, train_data_dir, validation_data_dir, nb_train_samples, nb_validation_samples, input_shape, img_width, img_height, epochs, batch_size)
def keras_fmin_fnct(space): with graph.as_default(): model = Sequential() model.add(Dense(units=space['units'], activation=space['activation'], input_shape=[4])) model.add(Dropout(rate=space['rate'])) model.add(Dense(units=2, activation='softmax')) model.compile(optimizer=SGD(lr=space['lr']), loss=space['loss'], metrics=['acc']) model.fit(x_train, y_train, validation_data=(x_test, y_test), batch_size=128, epochs=10, verbose=0) score, acc = model.evaluate(x_test, y_test, batch_size=128) return {'loss': -acc, 'status': STATUS_OK, 'model': model}
def mlp(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) result_dir = kw.pop('result_dir', None) job_id = kw.pop('job_id', None) project = project_business.get_by_id(project_id) ow = ownership_business.get_ownership_by_owned_item(project, 'project') user_ID = ow.user.user_ID f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] with graph.as_default(): return mlp_main(result_sds, project_id, job_id, user_ID, result_dir, x_train, y_train, x_val, y_val, x_test, y_test, f, e)
def export(job_id, user_ID): """ export model for tf serving :param job_id: str/ObjectId :return: """ result_dir, h5_filename = get_results_dir_by_job_id(job_id, user_ID) # result_sds = staging_data_set_business.get_by_job_id(job_id) model_dir = os.path.join(result_dir + '/', 'model.json') weights_dir = os.path.join(result_dir + '/', h5_filename) with open(model_dir, 'r') as f: data = json.load(f) json_string = json.dumps(data) # new_g = tf.Graph() with graph.as_default(): model = model_from_json(json_string) # model.load_weights(weights_dir) # working_dir = MODEL_EXPORT_BASE # export_base_path = os.path.join(working_dir, str(result_sds.id)) version = keras_saved_model.export(model, result_dir, weights_dir) return result_dir, version
def mnist_irnn(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] x_train = x_train.reshape(x_train.shape[0], -1, 1) x_test = x_test.reshape(x_test.shape[0], -1, 1) x_val = x_test x_train_shape = x_train.shape input_shape = x_train_shape[1:] num_classes = y_train.shape[1] hidden_units = 100 learning_rate = 1e-6 with graph.as_default(): model = Sequential() model.add( SimpleRNN( hidden_units, kernel_initializer=initializers.RandomNormal(stddev=0.001), recurrent_initializer=initializers.Identity(gain=1.0), activation='relu', input_shape=input_shape)) model.add(Dense(num_classes)) model.add(Activation('softmax')) rmsprop = RMSprop(lr=learning_rate) model.compile(loss='categorical_crossentropy', optimizer=rmsprop, metrics=['accuracy']) # callback to save metrics batch_print_callback = LambdaCallback( on_epoch_end=lambda epoch, logs: logger_service.log_epoch_end( epoch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[ batch_print_callback, best_checkpoint, general_checkpoint ], verbose=0, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) return {'score': score, 'history': history.history}
def mnist_mlp(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] num_classes = y_train.shape[1] # 获取 img 的格式 x_train_shape = x_train.shape if x_train_shape[1] > 3: # 格式为 (None, img_rows, img_cols, 1) x_train = x_train.reshape(-1, x_train_shape[1] * x_train_shape[2]) x_test = x_test.reshape(-1, x_train_shape[1] * x_train_shape[2]) x_val = x_test else: # 格式为 (None, 1, img_rows, img_cols) x_train = x_train.reshape(-1, x_train_shape[2] * x_train_shape[3]) x_test = x_test.reshape(-1, x_train_shape[2] * x_train_shape[3]) x_val = x_test # print(x_train.shape) with graph.as_default(): model = Sequential() model.add( Dense(512, activation='relu', input_shape=(x_train.shape[1], ))) model.add(Dropout(0.2)) model.add(Dense(512, activation='relu')) model.add(Dropout(0.2)) model.add(Dense(num_classes, activation='softmax')) model.compile(loss='categorical_crossentropy', optimizer=RMSprop(), metrics=['accuracy']) # callback to save metrics batch_print_callback = LambdaCallback( on_epoch_end=lambda epoch, logs: logger_service.log_epoch_end( epoch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[ batch_print_callback, best_checkpoint, general_checkpoint ], verbose=0, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) return {'score': score, 'history': history.history}
def convnet(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] input_shape = x_train.shape[1:] output_units = y_train.shape[-1] with graph.as_default(): model = Sequential() model.add(Conv2D(32, (3, 3), activation='relu', input_shape=input_shape)) model.add(Conv2D(32, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(Conv2D(64, (3, 3), activation='relu')) model.add(MaxPooling2D(pool_size=(2, 2))) model.add(Dropout(0.25)) model.add(Flatten()) model.add(Dense(256, activation='relu')) model.add(Dropout(0.5)) model.add(Dense(output_units, activation='softmax')) sgd = SGD(lr=0.01, decay=1e-6, momentum=0.9, nesterov=True) model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy']) # callback to save metrics batch_print_callback = LambdaCallback(on_epoch_end= lambda epoch, logs: logger_service.log_epoch_end(epoch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[batch_print_callback, best_checkpoint, general_checkpoint], verbose=0, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) return {'score': score, 'history': history.history}
def keras_seq(conf, input, **kw): """ a general implementation of sequential model of keras :param conf: config dict :return: """ result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) job_id = kw.pop('job_id', None) project = project_business.get_by_id(project_id) ow = ownership_business.get_ownership_by_owned_item(project, 'project') user_ID = ow.user.user_ID print('conf') print(conf) result_dir = kw.pop('result_dir', None) if result_sds is None: raise RuntimeError('no result sds id passed to model') if project_id is None: raise RuntimeError('no project id passed to model') with graph.as_default(): model = Sequential() ls = conf['layers'] comp = conf['compile'] f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] training_logger = logger_service.TrainingLogger(f['args']['epochs'], project_id, job_id, user_ID, result_sds) # TODO add validator # op = comp['optimizer'] # loop to add layers for l in ls: # get layer class from keras layer_class = getattr(layers, l['name']) # add layer model.add(layer_class(**l['args'])) # optimiser # sgd_class = getattr(optimizers, op['name']) # sgd = sgd_class(**op['args']) # define the metrics # compile model.compile(**comp['args']) # callback to save metrics batch_print_callback = LambdaCallback(on_epoch_begin= lambda epoch, logs: training_logger.log_epoch_begin( epoch, logs), on_epoch_end= lambda epoch, logs: training_logger.log_epoch_end( epoch, logs), on_batch_end= lambda batch, logs: training_logger.log_batch_end( batch, logs) ) # checkpoint to save best weight best_checkpoint = MyModelCheckpoint( os.path.abspath(os.path.join(result_dir, 'best.hdf5')), save_weights_only=True, verbose=1, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MyModelCheckpoint( os.path.abspath(os.path.join(result_dir, 'latest.hdf5')), save_weights_only=True, verbose=1) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[batch_print_callback, best_checkpoint, general_checkpoint], verbose=0, **f['args']) # testing score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) keras_saved_model.save_model(result_dir, model) return {'score': score, 'history': history.history}
def neural_style_transfer(args, project_id, file_url): # Path to the image to transform. base_image_path = args.get('base_image_path') # Path to the style reference image. style_reference_image_path = args.get('style_reference_image_path') # Prefix for the saved results. result_prefix = args.get('result_prefix') # Number of iterations to run. iterations = args.get('iter', 3) # these are the weights of the different loss components # content_weight content_weight = args.get('content_weight', 0.025) # Style weight. style_weight = args.get('style_weight', 2.0) # Total Variation weight. total_variation_weight = args.get('tv_weight', 1.0) # dimensions of the generated picture. width, height = load_img(base_image_path).size img_nrows = 400 img_ncols = int(width * img_nrows / height) with graph.as_default(): # this Evaluator class makes it possible # to compute loss and gradients in one pass # while retrieving them via two separate functions, # "loss" and "grads". This is done because scipy.optimize # requires separate functions for loss and gradients, # but computing them separately would be inefficient. class Evaluator(object): def __init__(self): self.loss_value = None self.grads_values = None def loss(self, x): assert self.loss_value is None loss_value, grad_values = eval_loss_and_grads(x) self.loss_value = loss_value self.grad_values = grad_values return self.loss_value def grads(self, x): assert self.loss_value is not None grad_values = np.copy(self.grad_values) self.loss_value = None self.grad_values = None return grad_values # util function to open, resize and format pictures into appropriate # tensors # 此步骤将img的channel顺序由RGB转到了BGR # 主要是因为 vgg模型当时是用caffe训练的,使用了opencv来加载图像, # 而opencv的加载顺序是 BGR # 结果是 VGG 的输入图像需要转换到 BGR模式 def preprocess_image(image_path): img = load_img(image_path, target_size=(img_nrows, img_ncols)) img = img_to_array(img) img = np.expand_dims(img, axis=0) img = vgg19.preprocess_input(img) return img # util function to convert a tensor into a valid image # 此步骤又从BGR转换回 RGB def deprocess_image(x): if K.image_data_format() == 'channels_first': x = x.reshape((3, img_nrows, img_ncols)) x = x.transpose((1, 2, 0)) else: x = x.reshape((img_nrows, img_ncols, 3)) # Remove zero-center by mean pixel x[:, :, 0] += 103.939 x[:, :, 1] += 116.779 x[:, :, 2] += 123.68 # 'BGR'->'RGB' x = x[:, :, ::-1] x = np.clip(x, 0, 255).astype('uint8') return x # compute the neural style loss # first we need to define 4 util functions # the gram matrix of an image tensor (feature-wise outer product) def gram_matrix(x): assert K.ndim(x) == 3 if K.image_data_format() == 'channels_first': features = K.batch_flatten(x) else: features = K.batch_flatten(K.permute_dimensions(x, (2, 0, 1))) gram = K.dot(features, K.transpose(features)) return gram # the "style loss" is designed to maintain # the style of the reference image in the generated image. # It is based on the gram matrices (which capture style) of # feature maps from the style reference image # and from the generated image def style_loss(style, combination): assert K.ndim(style) == 3 assert K.ndim(combination) == 3 S = gram_matrix(style) C = gram_matrix(combination) channels = 3 size = img_nrows * img_ncols return K.sum(K.square(S - C)) / (4. * (channels**2) * (size**2)) # an auxiliary loss function # designed to maintain the "content" of the # base image in the generated image def content_loss(base, combination): return K.sum(K.square(combination - base)) # the 3rd loss function, total variation loss, # designed to keep the generated image locally coherent def total_variation_loss(x): assert K.ndim(x) == 4 if K.image_data_format() == 'channels_first': a = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] - x[:, :, 1:, :img_ncols - 1]) b = K.square(x[:, :, :img_nrows - 1, :img_ncols - 1] - x[:, :, :img_nrows - 1, 1:]) else: a = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] - x[:, 1:, :img_ncols - 1, :]) b = K.square(x[:, :img_nrows - 1, :img_ncols - 1, :] - x[:, :img_nrows - 1, 1:, :]) return K.sum(K.pow(a + b, 1.25)) def eval_loss_and_grads(x): if K.image_data_format() == 'channels_first': x = x.reshape((1, 3, img_nrows, img_ncols)) else: x = x.reshape((1, img_nrows, img_ncols, 3)) outs = f_outputs([x]) loss_value = outs[0] if len(outs[1:]) == 1: grad_values = outs[1].flatten().astype('float64') else: grad_values = np.array(outs[1:]).flatten().astype('float64') return loss_value, grad_values # get tensor representations of our images base_image = K.variable(preprocess_image(base_image_path)) style_reference_image = K.variable( preprocess_image(style_reference_image_path)) # this will contain our generated image if K.image_data_format() == 'channels_first': combination_image = K.placeholder((1, 3, img_nrows, img_ncols)) else: combination_image = K.placeholder((1, img_nrows, img_ncols, 3)) # combine the 3 images into a single Keras tensor input_tensor = K.concatenate( [base_image, style_reference_image, combination_image], axis=0) # build the VGG16 network with our 3 images as input # the model will be loaded with pre-trained ImageNet weights model = vgg19.VGG19(input_tensor=input_tensor, weights='imagenet', include_top=False) print('Model loaded.') # get the symbolic outputs of each "key" layer (we gave them unique names). outputs_dict = dict([(layer.name, layer.output) for layer in model.layers]) # combine these loss functions into a single scalar loss = K.variable(0.) layer_features = outputs_dict['block5_conv2'] base_image_features = layer_features[0, :, :, :] combination_features = layer_features[2, :, :, :] loss += content_weight * content_loss(base_image_features, combination_features) feature_layers = [ 'block1_conv1', 'block2_conv1', 'block3_conv1', 'block4_conv1', 'block5_conv1' ] for layer_name in feature_layers: layer_features = outputs_dict[layer_name] style_reference_features = layer_features[1, :, :, :] combination_features = layer_features[2, :, :, :] sl = style_loss(style_reference_features, combination_features) loss += (style_weight / len(feature_layers)) * sl loss += total_variation_weight * total_variation_loss( combination_image) # get the gradients of the generated image wrt the loss grads = K.gradients(loss, combination_image) outputs = [loss] if isinstance(grads, (list, tuple)): outputs += grads else: outputs.append(grads) f_outputs = K.function([combination_image], outputs) evaluator = Evaluator() # run scipy-based optimization (L-BFGS) over the pixels of the generated # image # so as to minimize the neural style loss x = preprocess_image(base_image_path) url = '' for i in range(iterations): print('Start of iteration', i) start_time = time.time() x, min_val, info = fmin_l_bfgs_b(evaluator.loss, x.flatten(), fprime=evaluator.grads, maxfun=20) print('Current loss value:', min_val) # save current generated image img = deprocess_image(x.copy()) fname = result_prefix + '_at_iteration_%d.png' % i imsave(fname, img) end_time = time.time() url = file_url + 'result_at_iteration_{}.png?predict=true'.format( i) logger_service.emit_message_url({'url': url, 'n': i}, project_id) print('Image saved as', fname) print('Iteration %d completed in %ds' % (i, end_time - start_time)) return {'url': url, 'n': iterations}
def imdb_lstm(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] max_features = input['max_features'] maxlen = input['maxlen'] x_train = sequence.pad_sequences(x_train, maxlen=maxlen) x_test = sequence.pad_sequences(x_test, maxlen=maxlen) x_val = x_test with graph.as_default(): model = Sequential() model.add(Embedding(max_features, 128)) model.add(LSTM(128, dropout=0.2, recurrent_dropout=0.2)) model.add(Dense(1, activation='sigmoid')) # try using different optimizers and different optimizer configs model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # callback to save metrics batch_print_callback = LambdaCallback( on_epoch_end=lambda epoch, logs: logger_service.log_epoch_end( epoch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[ batch_print_callback, best_checkpoint, general_checkpoint ], verbose=0, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) return {'score': score, 'history': history.history}
def imdb_fasttext(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] # Set parameters: # ngram_range = 2 will add bi-grams features ngram_range = input['ngram_range'] max_features = input['max_features'] maxlen = input['maxlen'] embedding_dims = 50 def create_ngram_set(input_list, ngram_value=2): """ Extract a set of n-grams from a list of integers. create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=2) {(4, 9), (4, 1), (1, 4), (9, 4)} create_ngram_set([1, 4, 9, 4, 1, 4], ngram_value=3) [(1, 4, 9), (4, 9, 4), (9, 4, 1), (4, 1, 4)] """ return set(zip(*[input_list[i:] for i in range(ngram_value)])) def add_ngram(sequences, token_indice, ngram_range=2): """ Augment the input list of list (sequences) by appending n-grams values. Example: adding bi-gram sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]] token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017} add_ngram(sequences, token_indice, ngram_range=2) [[1, 3, 4, 5, 1337, 2017], [1, 3, 7, 9, 2, 1337, 42]] Example: adding tri-gram sequences = [[1, 3, 4, 5], [1, 3, 7, 9, 2]] token_indice = {(1, 3): 1337, (9, 2): 42, (4, 5): 2017, (7, 9, 2): 2018} add_ngram(sequences, token_indice, ngram_range=3) [[1, 3, 4, 5, 1337], [1, 3, 7, 9, 2, 1337, 2018]] """ new_sequences = [] for input_list in sequences: new_list = input_list[:] for i in range(len(new_list) - ngram_range + 1): for ngram_value in range(2, ngram_range + 1): ngram = tuple(new_list[i:i + ngram_value]) if ngram in token_indice: new_list.append(token_indice[ngram]) new_sequences.append(new_list) return new_sequences if ngram_range > 1: print('Adding {}-gram features'.format(ngram_range)) # Create set of unique n-gram from the training set. ngram_set = set() for input_list in x_train: for i in range(2, ngram_range + 1): set_of_ngram = create_ngram_set(input_list, ngram_value=i) ngram_set.update(set_of_ngram) # Dictionary mapping n-gram token to a unique integer. # Integer values are greater than max_features in order # to avoid collision with existing features. start_index = max_features + 1 token_indice = {v: k + start_index for k, v in enumerate(ngram_set)} indice_token = {token_indice[k]: k for k in token_indice} # max_features is the highest integer that could be found in the # dataset. max_features = np.max(list(indice_token.keys())) + 1 # Augmenting x_train and x_test with n-grams features x_train = add_ngram(x_train, token_indice, ngram_range) x_test = add_ngram(x_test, token_indice, ngram_range) # print('Average train sequence length: {}'.format( # np.mean(list(map(len, x_train)), dtype=int))) # print('Average test sequence length: {}'.format( # np.mean(list(map(len, x_test)), dtype=int))) print('Pad sequences (samples x time)') x_train = sequence.pad_sequences(x_train, maxlen=maxlen) x_test = sequence.pad_sequences(x_test, maxlen=maxlen) x_val = x_test print('x_train shape:', x_train.shape) print('x_test shape:', x_test.shape) with graph.as_default(): model = Sequential() # we start off with an efficient embedding layer which maps # our vocab indices into embedding_dims dimensions model.add(Embedding(max_features, embedding_dims, input_length=maxlen)) # we add a GlobalAveragePooling1D, which will average the embeddings # of all words in the document model.add(GlobalAveragePooling1D()) # We project onto a single unit output layer, and squash it with a # sigmoid: model.add(Dense(1, activation='sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # callback to save metrics batch_print_callback = LambdaCallback(on_epoch_end= lambda epoch, logs: logger_service.log_epoch_end( epoch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[batch_print_callback, best_checkpoint, general_checkpoint], verbose=0, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) return { 'score': score, 'history': history.history}
def imdb_cnn_lstm(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] # Embedding embedding_size = 128 max_features = input['max_features'] maxlen = input['maxlen'] x_train = sequence.pad_sequences(x_train, maxlen=maxlen) x_test = sequence.pad_sequences(x_test, maxlen=maxlen) x_val = x_test # set parameters: # Convolution kernel_size = 5 filters = 64 pool_size = 4 # LSTM lstm_output_size = 70 with graph.as_default(): model = Sequential() model.add(Embedding(max_features, embedding_size, input_length=maxlen)) model.add(Dropout(0.25)) model.add(Conv1D(filters, kernel_size, padding='valid', activation='relu', strides=1)) model.add(MaxPooling1D(pool_size=pool_size)) model.add(LSTM(lstm_output_size)) model.add(Dense(1)) model.add(Activation('sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # callback to save metrics batch_print_callback = LambdaCallback(on_epoch_end= lambda epoch, logs: logger_service.log_epoch_end( epoch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[batch_print_callback, best_checkpoint, general_checkpoint], verbose=0, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) return { 'score': score, 'history': history.history}
def imdb_cnn(conf, input, **kw): result_sds = kw.pop('result_sds', None) project_id = kw.pop('project_id', None) f = conf['fit'] e = conf['evaluate'] x_train = input['x_tr'] y_train = input['y_tr'] x_val = input['x_te'] y_val = input['y_te'] x_test = input['x_te'] y_test = input['y_te'] max_features = input['max_features'] maxlen = input['maxlen'] x_train = sequence.pad_sequences(x_train, maxlen=maxlen) x_test = sequence.pad_sequences(x_test, maxlen=maxlen) x_val = x_test # set parameters: embedding_dims = 50 filters = 250 kernel_size = 3 hidden_dims = 250 with graph.as_default(): model = Sequential() model.add(Embedding(max_features, embedding_dims, input_length=maxlen)) model.add(Dropout(0.2)) # we add a Convolution1D, which will learn filters # word group filters of size filter_length: model.add( Conv1D(filters, kernel_size, padding='valid', activation='relu', strides=1)) # we use max pooling: model.add(GlobalMaxPooling1D()) # We add a vanilla hidden layer: model.add(Dense(hidden_dims)) model.add(Dropout(0.2)) model.add(Activation('relu')) # We project onto a single unit output layer, and squash it with a # sigmoid: model.add(Dense(1)) model.add(Activation('sigmoid')) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) # callback to save metrics batch_print_callback = LambdaCallback( on_epoch_end=lambda epoch, logs: logger_service.log_epoch_end( epoch, logs, result_sds, project_id)) # checkpoint to save best weight best_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0, save_best_only=True) # checkpoint to save latest weight general_checkpoint = MongoModelCheckpoint(result_sds=result_sds, verbose=0) # training history = model.fit(x_train, y_train, validation_data=(x_val, y_val), callbacks=[ batch_print_callback, best_checkpoint, general_checkpoint ], verbose=0, **f['args']) score = model.evaluate(x_test, y_test, **e['args']) # weights = model.get_weights() config = model.get_config() logger_service.log_train_end(result_sds, model_config=config, score=score, history=history.history) return {'score': score, 'history': history.history}