SOM神经网络图像分类tensorflow实现

import tensorflow as tf import numpy as np from tqdm import tqdm import re inputshape = 25088 class SOM(object): _trained = False def __init__(self, m, n,dim, n_iterations=100, alpha=None, sigma=None): self._m = m self._n = n self.dim = dim if alpha is None: alpha = 0.3 else: alpha = float(alpha) if sigma is None: sigma = max(m, n) / 2.0 else: sigma = float(sigma) self._n_iterations = abs(int(n_iterations)) self._centroid_grid = np.zeros(shape=(m,n)) self.loaded_weights = None ##INITIALIZE GRAPH self._graph = tf.Graph() ##POPULATE GRAPH WITH NECESSARY COMPONENTS with self._graph.as_default(): self._weightage_vects = tf.Variable(tf.random_normal( [m * n, dim],mean=0.5)) self._location_vects = tf.constant(np.array( list(self._neuron_locations(m, n)))) self._vect_input = tf.placeholder("float", [inputshape]) #输入的图片特征向量 self._dense_input,self._output,self._dense_weights = self.dense(self._vect_input) self._iter_input = tf.placeholder("float") winner_index = tf.argmin(tf.sqrt(tf.reduce_sum( tf.square(tf.subtract(self._weightage_vects, tf.stack( [self._dense_input[0] for i in range(m * n)]))), 1)), 0) slice_input = tf.pad(tf.reshape(winner_index, [1]), np.array([[0, 1]])) self.winner_loc = tf.reshape(tf.slice(self._location_vects, slice_input, tf.constant(np.array([1, 2]),dtype=tf.int64)), [2])#等于self._location_vects[winner_index] print(self.winner_loc) learning_rate_op = tf.subtract(1.0, tf.divide(self._iter_input, self._n_iterations)) _alpha_op = learning_rate_op*alpha _sigma_op = sigma*learning_rate_op bmu_distance_squares = tf.reduce_sum(tf.square(tf.subtract( self._location_vects, tf.stack( [self.winner_loc for i in range(m * n)]))), 1)#领域距离的平方向量 neighbourhood_func = tf.exp(tf.negative(tf.divide(tf.cast( bmu_distance_squares, "float32"), tf.square(_sigma_op)))) learning_rate_op = tf.multiply(_alpha_op, neighbourhood_func) #参数向量 learning_rate_multiplier = tf.stack([tf.tile(tf.slice( learning_rate_op, np.array([i]), np.array([1])), [dim]) for i in range(m * n)]) weightage_delta = tf.multiply( learning_rate_multiplier, tf.subtract(tf.stack([self._dense_input[0] for i in range(m * n)]), self._weightage_vects)) #增量矩阵 new_weightages_op = tf.add(self._weightage_vects, weightage_delta) cross_entropy = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=self._output,labels=[self.winner_loc[0]+self.winner_loc[1]]) train_step = tf.train.ProximalGradientDescentOptimizer(learning_rate=_alpha_op).minimize(cross_entropy) self._training_op = tf.group(tf.assign(self._weightage_vects, new_weightages_op),train_step) ##INITIALIZE SESSION self._sess = tf.Session() ##INITIALIZE VARIABLES init_op = tf.global_variables_initializer() self._sess.run(init_op) def _neuron_locations(self, m, n): for i in range(m): for j in range(n): yield np.array([i, j]) def train(self, input_vects): for iter_no in tqdm(range(self._n_iterations)): # Train with each vector one by one for input_vect in input_vects: # print(dense_vect.shape) self._sess.run(self._training_op, feed_dict={self._vect_input: input_vect, self._iter_input: iter_no}) # Store a centroid grid for easy retrieval later on self._locations = self._sess.run(self._location_vects) self._centroid_grid = self._sess.run(self._weightage_vects) self._dense_vects = self._sess.run(self._dense_weights) self._trained = True def save_weights(self,filename): need_save = [self._dense_vects,self._centroid_grid] if filename.endswith('npy'): np.save(filename,need_save) elif filename.endswith('txt'): with open(filename,'w') as f: f.write(str(need_save)) elif filename.endswith('json'): import json with open(filename,'w') as f: json.dump(str(need_save),f) else: raise TypeError('cannot identify this type file') def load_weights(self,filename): if filename.endswith('npy'): weigths = np.load(filename) elif filename.endswith('txt'): with open(filename,'r') as f: weigths = eval(f.read()) elif filename.endswith('json'): import json with open(filename,'r') as f: weigths = json.load(f) else: raise TypeError('cannot identify this type file') self.loaded_weights = weigths def predict(self,input_vects): if self.loaded_weights is None: raise RuntimeError('the weights not loaded') to_return = [] locations =list(self._neuron_locations(self._m,self._n)) dense_vects = np.dot(input_vects,self.loaded_weights[0])+0.1 for vect in dense_vects: min_index = min([i for i in range(len(self.loaded_weights[1]))], key=lambda x: np.linalg.norm(vect - self.loaded_weights[1][x])) to_return.append(locations[min_index]) return to_return def evaluate(self,name_data,predict_data,classes): analysis = {} for i in range(self._m * self._n): each_class = {} for clas in set(classes): each_class[clas] = 0 analysis[i] = each_class for name, result in zip(name_data, predict_data): idx = int(result[0]*self._n+result[1]) analysis[idx][re.search("|".join(classes), name).group()] += 1 score = {} for key, value in analysis.items(): max_item = max(value.items(), key=lambda x: x[1]) score[key] = {max_item[0]: max_item[1] / sum(value.values())} return analysis,score def dense(self,inputs_vect): inputs_vect = tf.expand_dims(inputs_vect,axis=0) w1 = tf.Variable(tf.random_normal([tf.shape(inputs_vect)[1], self.dim])) b1 = tf.constant(0.1,shape=[self.dim]) z1 = tf.matmul(inputs_vect,w1)+b1 a1 = tf.nn.relu(z1) w2 = tf.Variable(tf.random_normal([self.dim, self._m*self._n])) b2 = tf.constant(0.1,shape=[self._m*self._n]) z2 = tf.matmul(a1,w2)+b2 return a1,z2,w1