Python keras.backend.variable() Examples
The following are 30 code examples for showing how to use keras.backend.variable(). 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: keras_bn_library Author: bnsnapper File: recurrent.py License: MIT License | 6 votes |
def build(self, input_shape): self.input_spec = [InputSpec(shape=input_shape)] self.input_dim = input_shape[2] self.W = self.init((self.output_dim, 4 * self.input_dim), name='{}_W'.format(self.name)) self.U = self.inner_init((self.input_dim, 4 * self.input_dim), name='{}_U'.format(self.name)) self.b = K.variable(np.hstack((np.zeros(self.input_dim), K.get_value(self.forget_bias_init((self.input_dim,))), np.zeros(self.input_dim), np.zeros(self.input_dim))), name='{}_b'.format(self.name)) self.A = self.init((self.input_dim, self.output_dim), name='{}_A'.format(self.name)) self.ba = K.zeros((self.output_dim,), name='{}_ba'.format(self.name)) self.trainable_weights = [self.W, self.U, self.b, self.A, self.ba] if self.initial_weights is not None: self.set_weights(self.initial_weights) del self.initial_weights
Example 2
Project: Coloring-greyscale-images Author: emilwallner File: AdamAccumulate.py License: MIT License | 6 votes |
def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999, epsilon=None, decay=0., amsgrad=False, accum_iters=1, **kwargs): if accum_iters < 1: raise ValueError('accum_iters must be >= 1') super(AdamAccumulate, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.decay = K.variable(decay, name='decay') if epsilon is None: epsilon = K.epsilon() self.epsilon = epsilon self.initial_decay = decay self.amsgrad = amsgrad self.accum_iters = K.variable(accum_iters, K.dtype(self.iterations)) self.accum_iters_float = K.cast(self.accum_iters, K.floatx())
Example 3
Project: neural-style-keras Author: robertomest File: training.py License: MIT License | 6 votes |
def get_total_loss(content_losses, style_losses, total_var_loss, content_weights, style_weights, tv_weights, class_targets): total_loss = K.variable(0.) # Compute content losses for loss in content_losses: weighted_loss = K.mean(K.gather(content_weights, class_targets) * loss) weighted_content_losses.append(weighted_loss) total_loss += weighted_loss # Compute style losses for loss in style_losses: weighted_loss = K.mean(K.gather(style_weights, class_targets) * loss) weighted_style_losses.append(weighted_loss) total_loss += weighted_loss # Compute tv loss weighted_tv_loss = K.mean(K.gather(tv_weights, class_targets) * total_var_loss) total_loss += weighted_tv_loss return (total_loss, weighted_content_losses, weighted_style_losses, weighted_tv_loss)
Example 4
Project: voxelmorph Author: voxelmorph File: layers.py License: GNU General Public License v3.0 | 6 votes |
def build(self, input_shape): # Create mean and count # These are weights because just maintaining variables don't get saved with the model, and we'd like # to have these numbers saved when we save the model. # But we need to make sure that the weights are untrainable. self.mean = self.add_weight(name='mean', shape=input_shape[1:], initializer='zeros', trainable=False) self.count = self.add_weight(name='count', shape=[1], initializer='zeros', trainable=False) # self.mean = K.zeros(input_shape[1:], name='mean') # self.count = K.variable(0.0, name='count') super(MeanStream, self).build(input_shape) # Be sure to call this somewhere!
Example 5
Project: keras-adabound Author: titu1994 File: adabound.py License: MIT License | 6 votes |
def __init__(self, lr=0.001, final_lr=0.1, beta_1=0.9, beta_2=0.999, gamma=1e-3, epsilon=None, decay=0., amsbound=False, weight_decay=0.0, **kwargs): super(AdaBound, self).__init__(**kwargs) if not 0. <= gamma <= 1.: raise ValueError("Invalid `gamma` parameter. Must lie in [0, 1] range.") with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.decay = K.variable(decay, name='decay') self.final_lr = final_lr self.gamma = gamma if epsilon is None: epsilon = K.epsilon() self.epsilon = epsilon self.initial_decay = decay self.amsbound = amsbound self.weight_decay = float(weight_decay) self.base_lr = float(lr)
Example 6
Project: image-analogies Author: awentzonline File: analogy.py License: MIT License | 6 votes |
def find_analogy_patches(a, a_prime, b, patch_size=3, patch_stride=1): '''This is for precalculating the analogy_loss Since A, A', and B never change we only need to calculate the patch matches once. ''' # extract patches from feature maps a_patches, a_patches_norm = patches.make_patches(K.variable(a), patch_size, patch_stride) a_prime_patches, a_prime_patches_norm = patches.make_patches(K.variable(a_prime), patch_size, patch_stride) b_patches, b_patches_norm = patches.make_patches(K.variable(b), patch_size, patch_stride) # find best patches and calculate loss p = patches.find_patch_matches(b_patches, b_patches_norm, a_patches / a_patches_norm) #best_patches = a_prime_patches[p] best_patches = K.reshape(a_prime_patches[p], K.shape(b_patches)) f = K.function([], best_patches) best_patches = f([]) return best_patches
Example 7
Project: keras-contrib Author: keras-team File: padam.py License: MIT License | 6 votes |
def __init__(self, lr=1e-1, beta_1=0.9, beta_2=0.999, epsilon=1e-8, decay=0., amsgrad=False, partial=1. / 8., **kwargs): if partial < 0 or partial > 0.5: raise ValueError( "Padam: 'partial' must be a positive float with a maximum " "value of `0.5`, since higher values will cause divergence " "during training." ) super(Padam, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.decay = K.variable(decay, name='decay') if epsilon is None: epsilon = K.epsilon() self.epsilon = epsilon self.partial = partial self.initial_decay = decay self.amsgrad = amsgrad
Example 8
Project: keras-contrib Author: keras-team File: lars.py License: MIT License | 6 votes |
def __init__(self, lr, momentum=0.9, weight_decay=0.0001, eeta=0.001, epsilon=0.0, nesterov=False, **kwargs): if momentum < 0.0: raise ValueError("momentum should be positive: %s" % momentum) if weight_decay < 0.0: raise ValueError("weight_decay is not positive: %s" % weight_decay) super(LARS, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') self.lr = K.variable(lr, name='lr') self.momentum = K.variable(momentum, name='momentum') self.weight_decay = K.variable(weight_decay, name='weight_decay') self.eeta = K.variable(eeta, name='eeta') self.epsilon = epsilon self.nesterov = nesterov
Example 9
Project: keras-contrib Author: keras-team File: yogi.py License: MIT License | 6 votes |
def __init__(self, lr=0.01, beta_1=0.9, beta_2=0.999, epsilon=1e-3, decay=0., **kwargs): super(Yogi, self).__init__(**kwargs) if beta_1 <= 0 or beta_1 >= 1: raise ValueError("beta_1 has to be in ]0, 1[") if beta_2 <= 0 or beta_2 >= 1: raise ValueError("beta_2 has to be in ]0, 1[") with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.decay = K.variable(decay, name='decay') if epsilon is None: epsilon = K.epsilon() if epsilon <= 0: raise ValueError("epsilon has to be larger than 0") self.epsilon = epsilon self.initial_decay = decay
Example 10
Project: keras-contrib Author: keras-team File: test_subpixelupscaling.py License: MIT License | 6 votes |
def test_sub_pixel_upscaling(scale_factor): num_samples = 2 num_row = 16 num_col = 16 input_dtype = K.floatx() nb_channels = 4 * (scale_factor ** 2) input_data = np.random.random((num_samples, nb_channels, num_row, num_col)) input_data = input_data.astype(input_dtype) if K.image_data_format() == 'channels_last': input_data = input_data.transpose((0, 2, 3, 1)) input_tensor = K.variable(input_data) expected_output = K.eval(KC.depth_to_space(input_tensor, scale=scale_factor)) layer_test(SubPixelUpscaling, kwargs={'scale_factor': scale_factor}, input_data=input_data, expected_output=expected_output, expected_output_dtype=K.floatx())
Example 11
Project: keras-contrib Author: keras-team File: backend_test.py License: MIT License | 6 votes |
def check_composed_tensor_operations(first_function_name, first_function_args, second_function_name, second_function_args, input_shape): ''' Creates a random tensor t0 with shape input_shape and compute t1 = first_function_name(t0, **first_function_args) t2 = second_function_name(t1, **second_function_args) with both Theano and TensorFlow backends and ensures the answers match. ''' val = np.random.random(input_shape) - 0.5 xth = KTH.variable(val) xtf = KTF.variable(val) yth = getattr(KCTH, first_function_name)(xth, **first_function_args) ytf = getattr(KCTF, first_function_name)(xtf, **first_function_args) zth = KTH.eval(getattr(KCTH, second_function_name)(yth, **second_function_args)) ztf = KTF.eval(getattr(KCTF, second_function_name)(ytf, **second_function_args)) assert zth.shape == ztf.shape assert_allclose(zth, ztf, atol=1e-05)
Example 12
Project: fancy-cnn Author: textclf File: embeddings.py License: MIT License | 5 votes |
def __init__(self, s=3, skip=True): self.skip = skip self.s = K.variable(s, name='s_constraint')
Example 13
Project: fancy-cnn Author: textclf File: embeddings.py License: MIT License | 5 votes |
def __init__(self, s=3, skip=True): self.skip = skip self.s = K.variable(s, name='s_constraint')
Example 14
Project: n2n-watermark-remove Author: zxq2233 File: model.py License: MIT License | 5 votes |
def __init__(self): self.gamma = K.variable(2.)
Example 15
Project: PiCamNN Author: PiSimo File: keras_yolo.py License: MIT License | 5 votes |
def yolo_eval(yolo_outputs, image_shape, max_boxes=10, score_threshold=.6, iou_threshold=.5): """Evaluate YOLO model on given input batch and return filtered boxes.""" box_xy, box_wh, box_confidence, box_class_probs = yolo_outputs boxes = yolo_boxes_to_corners(box_xy, box_wh) boxes, scores, classes = yolo_filter_boxes( boxes, box_confidence, box_class_probs, threshold=score_threshold) # Scale boxes back to original image shape. height = image_shape[0] width = image_shape[1] image_dims = K.stack([height, width, height, width]) image_dims = K.reshape(image_dims, [1, 4]) boxes = boxes * image_dims # TODO: Something must be done about this ugly hack! max_boxes_tensor = K.variable(max_boxes, dtype='int32') K.get_session().run(tf.variables_initializer([max_boxes_tensor])) nms_index = tf.image.non_max_suppression( boxes, scores, max_boxes_tensor, iou_threshold=iou_threshold) boxes = K.gather(boxes, nms_index) scores = K.gather(scores, nms_index) classes = K.gather(classes, nms_index) return boxes, scores, classes
Example 16
Project: deep-smoke-machine Author: CMU-CREATE-Lab File: resnet_152_keras.py License: BSD 3-Clause "New" or "Revised" License | 5 votes |
def build(self, input_shape): self.input_spec = [InputSpec(shape=input_shape)] shape = (int(input_shape[self.axis]),) self.gamma = K.variable(self.gamma_init(shape), name='%s_gamma' % self.name) self.beta = K.variable(self.beta_init(shape), name='%s_beta' % self.name) self.trainable_weights = [self.gamma, self.beta] if self.initial_weights is not None: self.set_weights(self.initial_weights) del self.initial_weights
Example 17
Project: timeception Author: noureldien File: resnet_152_keras.py License: GNU General Public License v3.0 | 5 votes |
def build(self, input_shape): self.input_spec = [InputSpec(shape=input_shape)] shape = (int(input_shape[self.axis]),) self.gamma = K.variable(self.gamma_init(shape), name='%s_gamma' % self.name) self.beta = K.variable(self.beta_init(shape), name='%s_beta' % self.name) self.trainable_weights = [self.gamma, self.beta] if self.initial_weights is not None: self.set_weights(self.initial_weights) del self.initial_weights
Example 18
Project: keras-adamw Author: OverLordGoldDragon File: optimizers.py License: MIT License | 5 votes |
def __init__(self, learning_rate=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False, model=None, zero_penalties=True, batch_size=32, total_iterations=0, total_iterations_wd=None, use_cosine_annealing=False, lr_multipliers=None, weight_decays=None, init_verbose=True, eta_min=0, eta_max=1, t_cur=0, **kwargs): if total_iterations > 1: weight_decays = _init_weight_decays(model, zero_penalties, weight_decays) self.initial_decay = kwargs.pop('decay', 0.0) self.epsilon = kwargs.pop('epsilon', K.epsilon()) learning_rate = kwargs.pop('lr', learning_rate) eta_t = kwargs.pop('eta_t', 1.) super(AdamW, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') self.learning_rate = K.variable(learning_rate, name='learning_rate') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.decay = K.variable(self.initial_decay, name='decay') self.eta_min = K.constant(eta_min, name='eta_min') self.eta_max = K.constant(eta_max, name='eta_max') self.eta_t = K.variable(eta_t, dtype='float32', name='eta_t') self.t_cur = K.variable(t_cur, dtype='int64', name='t_cur') self.batch_size = batch_size self.total_iterations = total_iterations self.total_iterations_wd = total_iterations_wd or total_iterations self.amsgrad = amsgrad self.lr_multipliers = lr_multipliers self.weight_decays = weight_decays or {} self.init_verbose = init_verbose self.use_cosine_annealing = use_cosine_annealing _check_args(self, total_iterations, use_cosine_annealing, weight_decays) self._init_lr = learning_rate # to print lr_mult setup self._init_notified = False
Example 19
Project: keras-adamw Author: OverLordGoldDragon File: optimizers.py License: MIT License | 5 votes |
def __init__(self, learning_rate=0.002, beta_1=0.9, beta_2=0.999, model=None, zero_penalties=True, batch_size=32, total_iterations=0, total_iterations_wd=None, use_cosine_annealing=False, lr_multipliers=None, weight_decays=None, init_verbose=True, eta_min=0, eta_max=1, t_cur=0, **kwargs): if total_iterations > 1: weight_decays = _init_weight_decays(model, zero_penalties, weight_decays) self.schedule_decay = kwargs.pop('schedule_decay', 0.004) self.epsilon = kwargs.pop('epsilon', K.epsilon()) learning_rate = kwargs.pop('lr', learning_rate) eta_t = kwargs.pop('eta_t', 1.) super(NadamW, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') self.m_schedule = K.variable(1., name='m_schedule') self.learning_rate = K.variable(learning_rate, name='learning_rate') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.eta_min = K.constant(eta_min, name='eta_min') self.eta_max = K.constant(eta_max, name='eta_max') self.eta_t = K.variable(eta_t, dtype='float32', name='eta_t') self.t_cur = K.variable(t_cur, dtype='int64', name='t_cur') self.batch_size = batch_size self.total_iterations = total_iterations self.total_iterations_wd = total_iterations_wd or total_iterations self.lr_multipliers = lr_multipliers self.weight_decays = weight_decays or {} self.use_cosine_annealing = use_cosine_annealing self.init_verbose = init_verbose _check_args(self, total_iterations, use_cosine_annealing, weight_decays) self._init_lr = learning_rate # to print lr_mult setup self._init_notified = False
Example 20
Project: keras-adamw Author: OverLordGoldDragon File: optimizers.py License: MIT License | 5 votes |
def __init__(self, learning_rate=0.01, momentum=0., nesterov=False, model=None, zero_penalties=True, batch_size=32, total_iterations=0, total_iterations_wd=None, use_cosine_annealing=False, lr_multipliers=None, weight_decays=None, init_verbose=True, eta_min=0, eta_max=1, t_cur=0, **kwargs): if total_iterations > 1: weight_decays = _init_weight_decays(model, zero_penalties, weight_decays) self.initial_decay = kwargs.pop('decay', 0.0) learning_rate = kwargs.pop('lr', learning_rate) eta_t = kwargs.pop('eta_t', 1.) super(SGDW, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') self.learning_rate = K.variable(learning_rate, name='learning_rate') self.momentum = K.variable(momentum, name='momentum') self.decay = K.variable(self.initial_decay, name='decay') self.eta_min = K.constant(eta_min, name='eta_min') self.eta_max = K.constant(eta_max, name='eta_max') self.eta_t = K.variable(eta_t, dtype='float32', name='eta_t') self.t_cur = K.variable(t_cur, dtype='int64', name='t_cur') self.batch_size = batch_size self.total_iterations = total_iterations self.total_iterations_wd = total_iterations_wd or total_iterations self.nesterov = nesterov self.lr_multipliers = lr_multipliers self.weight_decays = weight_decays or {} self.init_verbose = init_verbose self.use_cosine_annealing = use_cosine_annealing _check_args(self, total_iterations, use_cosine_annealing, weight_decays) self._init_lr = learning_rate # to print lr_mult setup self._init_notified = False
Example 21
Project: keras-adamw Author: OverLordGoldDragon File: optimizers_225.py License: MIT License | 5 votes |
def __init__(self, lr=0.001, beta_1=0.9, beta_2=0.999, amsgrad=False, epsilon=None, decay=0.0, model=None, zero_penalties=True, batch_size=32, total_iterations=0, total_iterations_wd=None, use_cosine_annealing=False, lr_multipliers=None, weight_decays=None, init_verbose=True, eta_min=0, eta_max=1, t_cur=0, **kwargs): if total_iterations > 1: weight_decays = _init_weight_decays(model, zero_penalties, weight_decays) eta_t = kwargs.pop('eta_t', 1.) super(AdamW, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.decay = K.variable(decay, name='decay') self.eta_min = K.constant(eta_min, name='eta_min') self.eta_max = K.constant(eta_max, name='eta_max') self.eta_t = K.variable(eta_t, dtype='float32', name='eta_t') self.t_cur = K.variable(t_cur, dtype='int64', name='t_cur') self.initial_decay = decay self.epsilon = epsilon or K.epsilon() self.batch_size = batch_size self.total_iterations = total_iterations self.total_iterations_wd = total_iterations_wd or total_iterations self.amsgrad = amsgrad self.lr_multipliers = lr_multipliers self.weight_decays = weight_decays or {} self.init_verbose = init_verbose self.use_cosine_annealing = use_cosine_annealing _check_args(self, total_iterations, use_cosine_annealing, weight_decays) self._init_lr = lr # to print lr_mult setup self._init_notified = False
Example 22
Project: keras-adamw Author: OverLordGoldDragon File: optimizers_225.py License: MIT License | 5 votes |
def __init__(self, lr=0.002, beta_1=0.9, beta_2=0.999, schedule_decay=0.004, epsilon=None, model=None, zero_penalties=True, batch_size=32, total_iterations=0, total_iterations_wd=None, use_cosine_annealing=False, lr_multipliers=None, weight_decays=None, init_verbose=True, eta_min=0, eta_max=1, t_cur=0, **kwargs): if total_iterations > 1: weight_decays = _init_weight_decays(model, zero_penalties, weight_decays) eta_t = kwargs.pop('eta_t', 1.) super(NadamW, self).__init__(**kwargs) with K.name_scope(self.__class__.__name__): self.iterations = K.variable(0, dtype='int64', name='iterations') self.m_schedule = K.variable(1., name='m_schedule') self.lr = K.variable(lr, name='lr') self.beta_1 = K.variable(beta_1, name='beta_1') self.beta_2 = K.variable(beta_2, name='beta_2') self.eta_min = K.constant(eta_min, name='eta_min') self.eta_max = K.constant(eta_max, name='eta_max') self.eta_t = K.variable(eta_t, dtype='float32', name='eta_t') self.t_cur = K.variable(t_cur, dtype='int64', name='t_cur') self.epsilon = epsilon or K.epsilon() self.schedule_decay = schedule_decay self.batch_size = batch_size self.total_iterations = total_iterations self.total_iterations_wd = total_iterations_wd or total_iterations self.lr_multipliers = lr_multipliers self.weight_decays = weight_decays or {} self.use_cosine_annealing = use_cosine_annealing self.init_verbose = init_verbose _check_args(self, total_iterations, use_cosine_annealing, weight_decays) self._init_lr = lr # to print lr_mult setup self._init_notified = False
Example 23
Project: Car-Recognition Author: foamliu File: scale_layer.py License: MIT License | 5 votes |
def build(self, input_shape): self.input_spec = [InputSpec(shape=input_shape)] shape = (int(input_shape[self.axis]),) # Compatibility with TensorFlow >= 1.0.0 self.gamma = K.variable(self.gamma_init(shape), name='{}_gamma'.format(self.name)) self.beta = K.variable(self.beta_init(shape), name='{}_beta'.format(self.name)) #self.gamma = self.gamma_init(shape, name='{}_gamma'.format(self.name)) #self.beta = self.beta_init(shape, name='{}_beta'.format(self.name)) self.trainable_weights = [self.gamma, self.beta] if self.initial_weights is not None: self.set_weights(self.initial_weights) del self.initial_weights
Example 24
Project: object-detection Author: kaka-lin File: test_tiny_yolo.py License: MIT License | 5 votes |
def yolo_non_max_suppression(scores, boxes, classes, max_boxes = 10, iou_threshold = 0.5): max_boxes_tensor = K.variable(max_boxes, dtype='int32') # tensor to be used in tf.image.non_max_suppression() K.get_session().run(tf.variables_initializer([max_boxes_tensor])) # initialize variable max_boxes_tensor # Use tf.image.non_max_suppression() to get the list of indices corresponding to boxes you keep nms_indices = tf.image.non_max_suppression(boxes, scores, max_boxes, iou_threshold) # Use K.gather() to select only nms_indices from scores, boxes and classes scores = K.gather(scores, nms_indices) boxes = K.gather(boxes, nms_indices) classes = K.gather(classes, nms_indices) return scores, boxes, classes
Example 25
Project: object-detection Author: kaka-lin File: model.py License: MIT License | 5 votes |
def yolo_non_max_suppression(scores, boxes, classes, max_boxes = 10, iou_threshold = 0.5): max_boxes_tensor = K.variable(max_boxes, dtype='int32') # tensor to be used in tf.image.non_max_suppression() K.get_session().run(tf.variables_initializer([max_boxes_tensor])) # initialize variable max_boxes_tensor # Use tf.image.non_max_suppression() to get the list of indices corresponding to boxes you keep nms_indices = tf.image.non_max_suppression(boxes, scores, max_boxes, iou_threshold) # Use K.gather() to select only nms_indices from scores, boxes and classes scores = K.gather(scores, nms_indices) boxes = K.gather(boxes, nms_indices) classes = K.gather(classes, nms_indices) return scores, boxes, classes
Example 26
Project: object-detection Author: kaka-lin File: keras_yolo.py License: MIT License | 5 votes |
def yolo_eval(yolo_outputs, image_shape, max_boxes=10, score_threshold=.6, iou_threshold=.5): """Evaluate YOLO model on given input batch and return filtered boxes.""" box_confidence, box_xy, box_wh, box_class_probs = yolo_outputs boxes = yolo_boxes_to_corners(box_xy, box_wh) boxes, scores, classes = yolo_filter_boxes( box_confidence, boxes, box_class_probs, threshold=score_threshold) # Scale boxes back to original image shape. height = image_shape[0] width = image_shape[1] image_dims = K.stack([height, width, height, width]) image_dims = K.reshape(image_dims, [1, 4]) boxes = boxes * image_dims # TODO: Something must be done about this ugly hack! max_boxes_tensor = K.variable(max_boxes, dtype='int32') K.get_session().run(tf.variables_initializer([max_boxes_tensor])) nms_index = tf.image.non_max_suppression( boxes, scores, max_boxes_tensor, iou_threshold=iou_threshold) boxes = K.gather(boxes, nms_index) scores = K.gather(scores, nms_index) classes = K.gather(classes, nms_index) return boxes, scores, classes
Example 27
Project: MachineLearning Author: mengli File: vaegan_cifar.py License: Apache License 2.0 | 5 votes |
def mean_normal(shape, mean=1., scale=0.02, name=None): return K.variable(np.random.normal(loc=mean, scale=scale, size=shape), name=name)
Example 28
Project: MachineLearning Author: mengli File: vaegan_svhn.py License: Apache License 2.0 | 5 votes |
def mean_normal(shape, mean=1., scale=0.02, name=None): return K.variable(np.random.normal(loc=mean, scale=scale, size=shape), name=name)
Example 29
Project: keras_bn_library Author: bnsnapper File: recurrent.py License: MIT License | 5 votes |
def build(self, input_shape): self.input_spec = [InputSpec(shape=input_shape)] input_dim = input_shape[2] self.input_dim = input_dim if self.stateful: self.reset_states() else: self.states = [None, None] self.states_dim = [self.input_dim, self.output_dim] self.weight_size = self.output_dim * 4 self.W = self.add_weight((input_dim, self.weight_size), initializer=self.init, name='{}_W'.format(self.name), regularizer=self.W_regularizer) self.U = self.add_weight((input_dim, self.weight_size), initializer=self.inner_init, name='{}_U'.format(self.name), regularizer=self.U_regularizer) def b_reg(shape, name=None): return K.variable(np.hstack((np.zeros(self.output_dim), K.get_value(self.forget_bias_init((self.output_dim,))), np.zeros(self.output_dim), np.zeros(self.output_dim))), name='{}_b'.format(self.name)) self.b = self.add_weight((self.weight_size,), initializer=b_reg, name='{}_b'.format(self.name), regularizer=self.b_regularizer) if self.initial_weights is not None: self.set_weights(self.initial_weights) del self.initial_weights self.built = True
Example 30
Project: wtte-rnn Author: ragulpr File: test_keras.py License: MIT License | 5 votes |
def test_keras_unstack_hack(): y_true_np = np.random.random([1, 3, 2]) y_true_np[:, :, 0] = 0 y_true_np[:, :, 1] = 1 y_true_keras = K.variable(y_true_np) y, u = wtte._keras_unstack_hack(y_true_keras) y_true_keras_new = K.stack([y, u], axis=-1) np.testing.assert_array_equal(K.eval(y_true_keras_new), y_true_np) # SANITY CHECK: Use pure Weibull data censored at C(ensoring point). # Should converge to the generating A(alpha) and B(eta) for each timestep