我们从Python开源项目中，提取了以下50个代码示例，用于说明如何使用numpy.float32()。
def classification_metrics(y, y_pred, threshold): metrics = {} metrics['threshold'] = threshold_from_predictions(y, y_pred, 0) metrics['np.std(y_pred)'] = np.std(y_pred) metrics['positive_frac_batch'] = float(np.count_nonzero(y == True)) / len(y) denom = np.count_nonzero(y == False) num = np.count_nonzero(np.logical_and(y == False, y_pred >= threshold)) if denom > 0: metrics['fpr'] = float(num) / float(denom) if any(y) and not all(y): metrics['auc'] = roc_auc_score(y, y_pred) y_pred_bool = y_pred >= threshold if (any(y_pred_bool) and not all(y_pred_bool)): metrics['precision'] = precision_score(np.array(y, dtype=np.float32), y_pred_bool) metrics['recall'] = recall_score(y, y_pred_bool) return metrics
def preprocess(image): """Takes an image and apply preprocess""" # ???????????? image = cv2.resize(image, (data_shape, data_shape)) # ?? BGR ? RGB image = image[:, :, (2, 1, 0)] # ?mean?????float image = image.astype(np.float32) # ? mean image -= np.array([123, 117, 104]) # ??? [batch-channel-height-width] image = np.transpose(image, (2, 0, 1)) image = image[np.newaxis, :] # ?? ndarray image = nd.array(image) return image
def remove_artifacts(self, image): """ Remove the connected components that are not within the parameters Operates in place :param image: sudoku's thresholded image w/o grid :return: None """ labeled, features = label(image, structure=CROSS) lbls = np.arange(1, features + 1) areas = extract_feature(image, labeled, lbls, np.sum, np.uint32, 0) sides = extract_feature(image, labeled, lbls, min_side, np.float32, 0, True) diags = extract_feature(image, labeled, lbls, diagonal, np.float32, 0, True) for index in lbls: area = areas[index - 1] / 255 side = sides[index - 1] diag = diags[index - 1] if side < 5 or side > 20 \ or diag < 15 or diag > 25 \ or area < 40: image[labeled == index] = 0 return None
def word_list_to_embedding(words, embeddings, embedding_dimension=50): ''' :param words: an n x (2*window_size + 1) matrix from data_to_mat :param embeddings: an embedding dictionary where keys are strings and values are embeddings; the output from embeddings_to_dict :param embedding_dimension: the dimension of the values in embeddings; in this assignment, embedding_dimension=50 :return: an n x ((2*window_size + 1)*embedding_dimension) matrix where each entry of the words matrix is replaced with its embedding ''' m, n = words.shape words = words.reshape((-1)) return np.array([embeddings[w] for w in words], dtype=np.float32).reshape(m, n*embedding_dimension) # # End Twitter Helper Functions #
def put_images_on_grid(images, shape=(16,8)): nrof_images = images.shape[0] img_size = images.shape[1] bw = 3 img = np.zeros((shape[1]*(img_size+bw)+bw, shape[0]*(img_size+bw)+bw, 3), np.float32) for i in range(shape[1]): x_start = i*(img_size+bw)+bw for j in range(shape[0]): img_index = i*shape[0]+j if img_index>=nrof_images: break y_start = j*(img_size+bw)+bw img[x_start:x_start+img_size, y_start:y_start+img_size, :] = images[img_index, :, :, :] if img_index>=nrof_images: break return img
def layout_tree(correlation): """Layout tree for visualization with e.g. matplotlib. Args: correlation: A [V, V]-shaped numpy array of latent correlations. Returns: A [V, 3]-shaped numpy array of spectral positions of vertices. """ assert len(correlation.shape) == 2 assert correlation.shape[0] == correlation.shape[1] assert correlation.dtype == np.float32 laplacian = -correlation np.fill_diagonal(laplacian, 0) np.fill_diagonal(laplacian, -laplacian.sum(axis=0)) evals, evects = scipy.linalg.eigh(laplacian, eigvals=[1, 2, 3]) assert np.all(evals > 0) assert evects.shape[1] == 3 return evects
def test_quantize_from_probs2(size, resolution): set_random_seed(make_seed(size, resolution)) probs = np.exp(np.random.random(size)).astype(np.float32) probs2 = probs.reshape((1, size)) quantized = quantize_from_probs2(probs2, resolution) assert quantized.shape == probs2.shape assert quantized.dtype == np.int8 assert np.all(quantized.sum(axis=1) == resolution) # Check that quantized result is closer to target than any other value. quantized = quantized.reshape((size, )) target = resolution * probs / probs.sum() distance = np.abs(quantized - target).sum() for combo in itertools.combinations(range(size), resolution): other = np.zeros(size, np.int8) for i in combo: other[i] += 1 assert other.sum() == resolution other_distance = np.abs(other - target).sum() assert distance <= other_distance
def sample_tree(self): """Samples a random tree. Returns: A pair (edges, edge_logits), where: edges: A list of (vertex, vertex) pairs. edge_logits: A [K]-shaped numpy array of edge logits. """ logger.info('TreeCatTrainer.sample_tree given %d rows', len(self._added_rows)) SERIES.sample_tree_num_rows.append(len(self._added_rows)) complete_grid = self._tree.complete_grid edge_logits = self.compute_edge_logits() assert edge_logits.shape[0] == complete_grid.shape[1] assert edge_logits.dtype == np.float32 edges = self.get_edges() edges = sample_tree(complete_grid, edge_logits, edges) return edges, edge_logits
def treecat_add_cell( feature_type, ragged_index, data_row, message, feat_probs, meas_probs, v, ): if feature_type == TY_MULTINOMIAL: beg, end = ragged_index[v:v + 2] feat_block = feat_probs[beg:end, :] meas_block = meas_probs[v, :] for c, count in enumerate(data_row[beg:end]): for _ in range(count): message *= feat_block[c, :] message /= meas_block feat_block[c, :] += np.float32(1) meas_block += np.float32(1) else: raise NotImplementedError
def __init__(self, data, tree_prior, config): """Initialize a model with an empty subsample. Args: data: An [N, V]-shaped numpy array of real-valued data. tree_prior: A [K]-shaped numpy array of prior edge log odds, where K is the number of edges in the complete graph on V vertices. config: A global config dict. """ assert isinstance(data, np.ndarray) data = np.asarray(data, np.float32) assert len(data.shape) == 2 N, V = data.shape D = config['model_latent_dim'] E = V - 1 # Number of edges in the tree. TreeTrainer.__init__(self, N, V, tree_prior, config) self._data = data self._latent = np.zeros([N, V, D], np.float32) # This is symmetric positive definite. self._vert_ss = np.zeros([V, D, D], np.float32) # This is arbitrary (not necessarily symmetric). self._edge_ss = np.zeros([E, D, D], np.float32) # This represents (count, mean, covariance). self._feat_ss = np.zeros([V, D, 1 + 1 + D], np.float32)
def observed_perplexity(self, counts): """Compute perplexity = exp(entropy) of observed variables. Perplexity is an information theoretic measure of the number of clusters or latent classes. Perplexity is a real number in the range [1, M], where M is model_num_clusters. Args: counts: A [V]-shaped array of multinomial counts. Returns: A [V]-shaped numpy array of perplexity. """ V, E, M, R = self._VEMR if counts is not None: counts = np.ones(V, dtype=np.int8) assert counts.shape == (V, ) assert counts.dtype == np.int8 assert np.all(counts > 0) observed_entropy = np.empty(V, dtype=np.float32) for v in range(V): beg, end = self._ragged_index[v:v + 2] probs = np.dot(self._feat_cond[beg:end, :], self._vert_probs[v, :]) observed_entropy[v] = multinomial_entropy(probs, counts[v]) return np.exp(observed_entropy)
def generate_model_file(num_rows, num_cols, num_cats=4, rate=1.0): """Generate a random model. Returns: The path to a gzipped pickled model. """ path = os.path.join(DATA, '{}-{}-{}-{:0.1f}.model.pkz'.format( num_rows, num_cols, num_cats, rate)) V = num_cols K = V * (V - 1) // 2 if os.path.exists(path): return path print('Generating {}'.format(path)) if not os.path.exists(DATA): os.makedirs(DATA) dataset_path = generate_dataset_file(num_rows, num_cols, num_cats, rate) dataset = pickle_load(dataset_path) table = dataset['table'] tree_prior = np.zeros(K, dtype=np.float32) config = make_config(learning_init_epochs=5) model = train_model(table, tree_prior, config) pickle_dump(model, path) return path
def calculate_loss(self, predictions, labels, weights=None, **unused_params): with tf.name_scope("loss_xent"): epsilon = 10e-6 if FLAGS.label_smoothing: float_labels = smoothing(labels) else: float_labels = tf.cast(labels, tf.float32) cross_entropy_loss = float_labels * tf.log(predictions + epsilon) + ( 1 - float_labels) * tf.log(1 - predictions + epsilon) cross_entropy_loss = tf.negative(cross_entropy_loss) if weights is not None: print cross_entropy_loss, weights weighted_loss = tf.einsum("ij,i->ij", cross_entropy_loss, weights) print "create weighted_loss", weighted_loss return tf.reduce_mean(tf.reduce_sum(weighted_loss, 1)) else: return tf.reduce_mean(tf.reduce_sum(cross_entropy_loss, 1))
def calculate_loss(self, predictions, support_predictions, labels, **unused_params): """ support_predictions batch_size x num_models x num_classes predictions = tf.reduce_mean(support_predictions, axis=1) """ model_count = tf.shape(support_predictions)[1] vocab_size = tf.shape(support_predictions)[2] mean_predictions = tf.reduce_mean(support_predictions, axis=1, keep_dims=True) support_labels = tf.tile(tf.expand_dims(tf.cast(labels, dtype=tf.float32), axis=1), multiples=[1,model_count,1]) support_means = tf.stop_gradient(tf.tile(mean_predictions, multiples=[1,model_count,1])) support_predictions = tf.reshape(support_predictions, shape=[-1,model_count*vocab_size]) support_labels = tf.reshape(support_labels, shape=[-1,model_count*vocab_size]) support_means = tf.reshape(support_means, shape=[-1,model_count*vocab_size]) ce_loss_fn = CrossEntropyLoss() # The cross entropy between predictions and ground truth cross_entropy_loss = ce_loss_fn.calculate_loss(support_predictions, support_labels, **unused_params) # The cross entropy between predictions and mean predictions divergence = ce_loss_fn.calculate_loss(support_predictions, support_means, **unused_params) loss = cross_entropy_loss * (1.0 - FLAGS.support_loss_percent) - divergence * FLAGS.support_loss_percent return loss
def get_batch_data(): # Load data X, Y = load_data() # calc total batch count num_batch = len(X) // hp.batch_size # Convert to tensor X = tf.convert_to_tensor(X, tf.int32) Y = tf.convert_to_tensor(Y, tf.float32) # Create Queues input_queues = tf.train.slice_input_producer([X, Y]) # create batch queues x, y = tf.train.batch(input_queues, num_threads=8, batch_size=hp.batch_size, capacity=hp.batch_size * 64, allow_smaller_final_batch=False) return x, y, num_batch # (N, T), (N, T), ()
def metrics(self, X, y): metrics = {} y_pred_pair, loss = self.predict_proba_with_loss(X, y) y_pred = y_pred_pair[:,1] ## From softmax pair to prob of catastrophe metrics['loss'] = loss threshold = self.threshold_from_data(X, y) metrics['threshold'] = threshold metrics['np.std(y_pred)'] = np.std(y_pred) denom = np.count_nonzero(y == False) num = np.count_nonzero(np.logical_and(y == False, y_pred >= threshold)) metrics['fpr'] = float(num) / float(denom) if any(y) and not all(y): metrics['auc'] = roc_auc_score(y, y_pred) y_pred_bool = y_pred >= threshold if (any(y_pred_bool) and not all(y_pred_bool)): metrics['precision'] = precision_score(np.array(y, dtype=np.float32), y_pred_bool) metrics['recall'] = recall_score(y, y_pred_bool) return metrics
def conv2d(x, num_filters, name, filter_size=(3, 3), stride=(1, 1), pad="SAME", dtype=tf.float32, collections=None): with tf.variable_scope(name): stride_shape = [1, stride[0], stride[1], 1] filter_shape = [filter_size[0], filter_size[1], int(x.get_shape()[3]), num_filters] # there are "num input feature maps * filter height * filter width" # inputs to each hidden unit fan_in = np.prod(filter_shape[:3]) # each unit in the lower layer receives a gradient from: # "num output feature maps * filter height * filter width" / # pooling size fan_out = np.prod(filter_shape[:2]) * num_filters # initialize weights with random weights w_bound = np.sqrt(6. / (fan_in + fan_out)) w = tf.get_variable("W", filter_shape, dtype, tf.random_uniform_initializer(-w_bound, w_bound), collections=collections) b = tf.get_variable("b", [1, 1, 1, num_filters], initializer=tf.constant_initializer(0.0), collections=collections) return tf.nn.conv2d(x, w, stride_shape, pad) + b
def __init__(self, ob_space, ac_space, layers=[256], **kwargs): self.x = x = tf.placeholder(tf.float32, [None] + list(ob_space)) rank = len(ob_space) if rank == 3: # pixel input for i in range(4): x = tf.nn.elu(conv2d(x, 32, "c{}".format(i + 1), [3, 3], [2, 2])) elif rank == 1: # plain features #x = tf.nn.elu(linear(x, 256, "l1", normalized_columns_initializer(0.01))) pass else: raise TypeError("observation space must have rank 1 or 3, got %d" % rank) x = flatten(x) for i, layer in enumerate(layers): x = tf.nn.elu(linear(x, layer, "l{}".format(i + 1), tf.contrib.layers.xavier_initializer())) self.logits = linear(x, ac_space, "action", tf.contrib.layers.xavier_initializer()) self.vf = tf.reshape(linear(x, 1, "value", tf.contrib.layers.xavier_initializer()), [-1]) self.sample = categorical_sample(self.logits, ac_space)[0, :] self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, tf.get_variable_scope().name) self.state_in = []
def __init__(self, ob_space, ac_space, size=256, **kwargs): self.x = x = tf.placeholder(tf.float32, [None] + list(ob_space)) for i in range(4): x = tf.nn.elu(conv2d(x, 32, "l{}".format(i + 1), [3, 3], [2, 2])) # introduce a "fake" batch dimension of 1 after flatten so that we can do GRU over time dim x = tf.expand_dims(flatten(x), 1) gru = rnn.GRUCell(size) h_init = np.zeros((1, size), np.float32) self.state_init = [h_init] h_in = tf.placeholder(tf.float32, [1, size]) self.state_in = [h_in] gru_outputs, gru_state = tf.nn.dynamic_rnn( gru, x, initial_state=h_in, sequence_length=[size], time_major=True) x = tf.reshape(gru_outputs, [-1, size]) self.logits = linear(x, ac_space, "action", normalized_columns_initializer(0.01)) self.vf = tf.reshape(linear(x, 1, "value", normalized_columns_initializer(1.0)), [-1]) self.state_out = [gru_state[:1]] self.sample = categorical_sample(self.logits, ac_space)[0, :] self.var_list = tf.get_collection(tf.GraphKeys.TRAINABLE_VARIABLES, tf.get_variable_scope().name)
def __init__(self, shape, name=None): """Takes input in uint8 format which is cast to float32 and divided by 255 before passing it to the model. On GPU this ensures lower data transfer times. Parameters ---------- shape: [int] shape of the tensor. name: str name of the underlying placeholder """ super().__init__(tf.placeholder(tf.uint8, [None] + list(shape), name=name)) self._shape = shape self._output = tf.cast(super().get(), tf.float32) / 255.0
def _get_dtype_maps(): """ Get dictionaries to map numpy data types to ITK types and the other way around. """ # Define pairs tmp = [ (np.float32, 'MET_FLOAT'), (np.float64, 'MET_DOUBLE'), (np.uint8, 'MET_UCHAR'), (np.int8, 'MET_CHAR'), (np.uint16, 'MET_USHORT'), (np.int16, 'MET_SHORT'), (np.uint32, 'MET_UINT'), (np.int32, 'MET_INT'), (np.uint64, 'MET_ULONG'), (np.int64, 'MET_LONG') ] # Create dictionaries map1, map2 = {}, {} for np_type, itk_type in tmp: map1[np_type.__name__] = itk_type map2[itk_type] = np_type.__name__ # Done return map1, map2
def test_op(self): logits = np.random.randn(self.sequence_length, self.batch_size, self.vocab_size) logits = logits.astype(np.float32) sequence_length = np.array([1, 2, 3, 4]) targets = np.random.randint(0, self.vocab_size, [self.sequence_length, self.batch_size]) losses = seq2seq_losses.cross_entropy_sequence_loss(logits, targets, sequence_length) with self.test_session() as sess: losses_ = sess.run(losses) # Make sure all losses not past the sequence length are > 0 np.testing.assert_array_less(np.zeros_like(losses_[:1, 0]), losses_[:1, 0]) np.testing.assert_array_less(np.zeros_like(losses_[:2, 1]), losses_[:2, 1]) np.testing.assert_array_less(np.zeros_like(losses_[:3, 2]), losses_[:3, 2]) # Make sure all losses past the sequence length are 0 np.testing.assert_array_equal(losses_[1:, 0], np.zeros_like(losses_[1:, 0])) np.testing.assert_array_equal(losses_[2:, 1], np.zeros_like(losses_[2:, 1])) np.testing.assert_array_equal(losses_[3:, 2], np.zeros_like(losses_[3:, 2]))
def position_encoding(sentence_size, embedding_size): """ Position Encoding described in section 4.1 of End-To-End Memory Networks (https://arxiv.org/abs/1503.08895). Args: sentence_size: length of the sentence embedding_size: dimensionality of the embeddings Returns: A numpy array of shape [sentence_size, embedding_size] containing the fixed position encodings for each sentence position. """ encoding = np.ones((sentence_size, embedding_size), dtype=np.float32) ls = sentence_size + 1 le = embedding_size + 1 for k in range(1, le): for j in range(1, ls): encoding[j-1, k-1] = (1.0 - j/float(ls)) - ( k / float(le)) * (1. - 2. * j/float(ls)) return encoding
def detect(self, img): img_h, img_w, _ = img.shape inputs = cv2.resize(img, (self.image_size, self.image_size)) inputs = cv2.cvtColor(inputs, cv2.COLOR_BGR2RGB).astype(np.float32) inputs = (inputs / 255.0) * 2.0 - 1.0 inputs = np.reshape(inputs, (1, self.image_size, self.image_size, 3)) result = self.detect_from_cvmat(inputs)[0] for i in range(len(result)): result[i][1] *= (1.0 * img_w / self.image_size) result[i][2] *= (1.0 * img_h / self.image_size) result[i][3] *= (1.0 * img_w / self.image_size) result[i][4] *= (1.0 * img_h / self.image_size) return result
def __init__(self, check_): self.img_feed = tf.placeholder(tf.float32) self.output_logits = tf.nn.softmax( models.foodv_test( self.img_feed, reg_val=0.0, is_train=False, dropout_p=1.0)) self.sess = tf.Session() self.checkpoint_name = check_ saver = tf.train.Saver() print("loading model...") saver.restore(self.sess, self.checkpoint_name) print("Model loaded !")
def has_tomatoes(self, im_path): # load the image im = Image.open(im_path) im = np.asarray(im, dtype=np.float32) im = self.prepare_image(im) # launch an inference with the image pred = self.sess.run( self.output_logits, feed_dict={ self.img_feed: im.eval( session=self.sess)}) if np.argmax(pred) == 0: print("NOT a tomato ! (confidence : ", pred[0, 0], "%)") else: print("We have a tomato ! (confidence : ", pred[0, 1], "%)")
def read_flow(path, filename): flowdata = None with open(path + filename + '.flo') as f: # Valid .flo file checker magic = np.fromfile(f, np.float32, count=1) if 202021.25 != magic: print 'Magic number incorrect. Invalid .flo file' else: # Reshape data into 3D array (columns, rows, bands) w = int(np.fromfile(f, np.int32, count=1)) h = int(np.fromfile(f, np.int32, count=1)) #print 'Reading {}.flo with shape: ({}, {}, 2)'.format(filename, h, w) flowdata = np.fromfile(f, np.float32, count=2*w*h) # NOTE: numpy shape(h, w, ch) is opposite to image shape(w, h, ch) flowdata = np.reshape(flowdata, (h, w, 2)) return flowdata
def gl_init(self): self.gl_vertex_shader_factory = functools.lru_cache(maxsize=None)(functools.partial(gl.Shader,GL_VERTEX_SHADER)) self.gl_fragment_shader_factory = functools.lru_cache(maxsize=None)(functools.partial(gl.Shader,GL_FRAGMENT_SHADER)) self.gl_program_factory = functools.lru_cache(maxsize=None)(GLProgram) self.gl_texture_factory = functools.lru_cache(maxsize=None)(gx.texture.GLTexture) array_table = {gx.VA_PTNMTXIDX:GLMatrixIndexArray()} array_table.update((attribute,array.gl_convert()) for attribute,array in self.array_table.items()) for shape in self.shapes: shape.gl_init(array_table) for material in self.materials: material.gl_init() for texture in self.textures: texture.gl_init(self.gl_texture_factory) self.gl_joints = [copy.copy(joint) for joint in self.joints] self.gl_joint_matrices = numpy.empty((len(self.joints),3,4),numpy.float32) self.gl_matrix_table = gl.TextureBuffer(GL_DYNAMIC_DRAW,GL_RGBA32F,(len(self.matrix_descriptors),3,4),numpy.float32) self.gl_update_matrix_table() self.gl_draw_objects = list(self.gl_generate_draw_objects(self.scene_graph)) self.gl_draw_objects.sort(key=lambda draw_object: draw_object.material.unknown0)
def reshapeWeights(self, weights, normalize=True, modifier=None): # reshape the weights matrix to a grid for visualization n_rows = int(np.sqrt(weights.shape[1])) n_cols = int(np.sqrt(weights.shape[1])) kernel_size = int(np.sqrt(weights.shape[0]/3)) weights_grid = np.zeros((int((np.sqrt(weights.shape[0]/3)+1)*n_rows), int((np.sqrt(weights.shape[0]/3)+1)*n_cols), 3), dtype=np.float32) for i in range(weights_grid.shape[0]/(kernel_size+1)): for j in range(weights_grid.shape[1]/(kernel_size+1)): index = i * (weights_grid.shape[0]/(kernel_size+1))+j if not np.isclose(np.sum(weights[:, index]), 0): if normalize: weights_grid[i * (kernel_size + 1):i * (kernel_size + 1) + kernel_size, j * (kernel_size + 1):j * (kernel_size + 1) + kernel_size]=\ (weights[:, index].reshape(kernel_size, kernel_size, 3) - np.min(weights[:, index])) / ((np.max(weights[:, index]) - np.min(weights[:, index])) + 1.e-6) else: weights_grid[i * (kernel_size + 1):i * (kernel_size + 1) + kernel_size, j * (kernel_size + 1):j * (kernel_size + 1) + kernel_size] =\ (weights[:, index].reshape(kernel_size, kernel_size, 3)) if modifier is not None: weights_grid[i * (kernel_size + 1):i * (kernel_size + 1) + kernel_size, j * (kernel_size + 1):j * (kernel_size + 1) + kernel_size] *= modifier[index] return weights_grid
def __init__(self, input_shape, output_shape): self.input_shape = input_shape self.input = np.zeros((output_shape[0], self.input_shape[0] * self.input_shape[1] * self.input_shape[2]),dtype=np.float32) self.output = np.zeros(output_shape, dtype=np.float32) self.output_raw = np.zeros_like(self.output) self.output_error = np.zeros_like(self.output) self.output_average = np.zeros(self.output.shape[1], dtype=np.float32) self.weights = np.random.normal(0, np.sqrt(2.0 / (self.output.shape[1] + self.input.shape[1])), size=(self.input.shape[1], self.output.shape[1])).astype(np.float32) self.gradient = np.zeros_like(self.weights) self.reconstruction = np.zeros_like(self.weights) self.errors = np.zeros_like(self.weights) self.output_ranks = np.zeros(self.output.shape[1], dtype=np.int32) self.learning_rate = 1 self.norm_limit = 0.1
def _normalise_data(self): self.train_x_mean = np.zeros(self.input_dim) self.train_x_std = np.ones(self.input_dim) self.train_y_mean = np.zeros(self.output_dim) self.train_y_std = np.ones(self.output_dim) if self.normalise_data: self.train_x_mean = np.mean(self.train_x, axis=0) self.train_x_std = np.std(self.train_x, axis=0) self.train_x_std[self.train_x_std == 0] = 1. self.train_x = (self.train_x - np.full(self.train_x.shape, self.train_x_mean, dtype=np.float32)) / \ np.full(self.train_x.shape, self.train_x_std, dtype=np.float32) self.test_x = (self.test_x - np.full(self.test_x.shape, self.train_x_mean, dtype=np.float32)) / \ np.full(self.test_x.shape, self.train_x_std, dtype=np.float32) self.train_y_mean = np.mean(self.train_y, axis=0) self.train_y_std = np.std(self.train_y, axis=0) if self.train_y_std == 0: self.train_y_std[self.train_y_std == 0] = 1. self.train_y = (self.train_y - self.train_y_mean) / self.train_y_std
def create_training_test_sets(self): # training set train_x = np.random.uniform(self.data_interval_left, self.data_interval_right, size=self.data_size) train_x = np.sort(train_x) train_y = self.true_f(train_x) + 3. * np.random.randn(self.data_size) self.train_x = [train_x.reshape((train_x.shape[0], 1))] self.train_y = [train_y.reshape((train_y.shape[0], 1))] # test set for visualisation self.test_x = np.arange(self.view_xrange[0], self.view_xrange[1], 0.01, dtype=np.float32) self.test_x = np.reshape(self.test_x, (self.test_x.shape[0], 1)) self.test_y = self.true_f(self.test_x) self.test_y = np.reshape(self.test_y, (self.test_y.shape[0], 1)) self.test_x = [self.test_x] self.test_y = [self.test_y]
def extract_digits(self, image): """ Extract digits from a binary image representing a sudoku :param image: binary image/sudoku :return: array of digits and their probabilities """ prob = np.zeros(4, dtype=np.float32) digits = np.zeros((4, 9, 9), dtype=object) for i in range(4): labeled, features = label(image, structure=CROSS) objs = find_objects(labeled) for obj in objs: roi = image[obj] # center of bounding box cy = (obj[0].stop + obj[0].start) / 2 cx = (obj[1].stop + obj[1].start) / 2 dists = cdist([[cy, cx]], CENTROIDS, 'euclidean') pos = np.argmin(dists) cy, cx = pos % 9, pos / 9 # 28x28 image, center relative to sudoku prediction = self.classifier.classify(morph(roi)) if digits[i, cy, cx] is 0: # Newly found digit digits[i, cy, cx] = prediction prob[i] += prediction[0, 0] elif prediction[0, 0] > digits[i, cy, cx][0, 0]: # Overlapping! (noise), choose the most probable prediction prob[i] -= digits[i, cy, cx][0, 0] digits[i, cy, cx] = prediction prob[i] += prediction[0, 0] image = np.rot90(image) logging.info(prob) return digits[np.argmax(prob)]
def _feature(image): """ It's faster but still accurate enough with DSIZE = 14. ~0.9983 precision and recall :param image: :return: raw pixels as feature vector """ image = cv2.resize(image, None, fx=DSIZE/28, fy=DSIZE/28, interpolation=cv2.INTER_LINEAR) ret = image.astype(np.float32) / 255 return ret.ravel()
def _zoning(image): """ It works better with DSIZE = 28 ~0.9967 precision and recall :param image: :return: #pixels/area ratio of each zone (7x7) as feature vector """ zones = [] for i in range(0, 28, 7): for j in range(0, 28, 7): roi = image[i:i+7, j:j+7] val = (np.sum(roi)/255) / 49. zones.append(val) return np.array(zones, np.float32)
def create_model(self, train_folder): """ Return the training set, its labels and the trained model :param train_folder: folder where to retrieve data :return: (train_set, train_labels, trained_model) """ digits = [] labels = [] for n in range(1, 10): folder = train_folder + str(n) samples = [pic for pic in os.listdir(folder) if os.path.isfile(os.path.join(folder, pic))] for sample in samples: image = cv2.imread(os.path.join(folder, sample)) # Expecting black on white image = 255 - cv2.cvtColor(image, cv2.COLOR_BGR2GRAY) _, image = cv2.threshold(image, 0, 255, cv2.THRESH_BINARY + cv2.THRESH_OTSU) feat = self.feature(image) digits.append(feat) labels.append(n) digits = np.array(digits, np.float32) labels = np.array(labels, np.float32) if cv2.__version__[0] == '2': model = cv2.KNearest() model.train(digits, labels) else: model = cv2.ml.KNearest_create() model.train(digits, cv2.ml.ROW_SAMPLE, labels) return digits, labels, model
def __init__(self, images, labels, fake_data=False): if fake_data: self._num_examples = 10000 else: assert images.shape[0] == labels.shape[0], ( "images.shape: %s labels.shape: %s" % (images.shape, labels.shape)) self._num_examples = images.shape[0] # Convert shape from [num examples, rows, columns, depth] # to [num examples, rows*columns] (assuming depth == 1) self.imageShape = images.shape[1:] self.imageChannels = self.imageShape[2] images = images.reshape(images.shape[0], images.shape[1] * images.shape[2] * images.shape[3]) # Convert from [0, 255] -> [0.0, 1.0]. images = images.astype(numpy.float32) images = numpy.multiply(images, 1.0 / 255.0) self._images = images self._labels = labels try: if len(numpy.shape(self._labels)) == 1: self._labels = dense_to_one_hot(self._labels,len(numpy.unique(self._labels))) except: traceback.print_exc() self._epochs_completed = 0 self._index_in_epoch = 0
