训练1024维特征的数据集合，multi-label， 转成多个1的onehot的label, 2个全连接。很直接的顺序编程，练习、便于理解
# Import your data#from tensorflow.examples.tutorials.mnist import input_dataimport osimport tensorflow as tffrom tensorflow import gfilefrom tensorflow import loggingfrom tensorflow import flagsfrom tensorflow import appimport numpy as np# Parameterslearning_rate = 0.001training_epochs = 5batch_size = 512display_step = 2# Network Parametersn_hidden_1 = 256 # 1st layer number of featuresn_hidden_2 = 128 # 2nd layer number of featuresn_input = 1024 #n_classes = 4716 # tf Graph inputx = tf.placeholder("float", [None, n_input])y = tf.placeholder("float", [None, n_classes])# Create modeldef multilayer_perceptron(x, weights, biases): # Hidden layer with RELU activation input_dim = len(x.get_shape()) - 1 model_input = tf.nn.l2_normalize(x, input_dim) layer_1 = tf.add(tf.matmul(model_input, weights['h1']), biases['b1']) layer_1 = tf.nn.relu(layer_1) # Hidden layer with RELU activation layer_2 = tf.add(tf.matmul(layer_1, weights['h2']), biases['b2']) layer_2 = tf.nn.relu(layer_2) layer_2 = tf.nn.dropout(layer_2, 0.5) # Output layer with linear activation out_layer = tf.matmul(layer_2, weights['out']) + biases['out'] return out_layer# Store layers weight & biasweights = { 'h1': tf.get_variable("wh1", shape=[n_input, n_hidden_1], initializer=tf.contrib.layers.xavier_initializer()), 'h2': tf.get_variable("wh2", shape=[n_hidden_1, n_hidden_2], initializer=tf.contrib.layers.xavier_initializer()), 'out': tf.get_variable("wout", shape=[n_hidden_2, n_classes], initializer=tf.contrib.layers.xavier_initializer())}biases = { 'b1': tf.Variable(tf.zeros([n_hidden_1])), 'b2': tf.Variable(tf.zeros([n_hidden_2])), 'out': tf.Variable(tf.zeros([n_classes]))}# Construct modelpred = multilayer_perceptron(x, weights, biases)# Define loss and optimizercost = tf.reduce_mean(tf.nn.sigmoid_cross_entropy_with_logits(logits=pred, labels=y))optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(cost)# Initializing the variablesinit = tf.global_variables_initializer()video_level_record_path="./video_level/train/" #NEED CHANGEsaver = tf.train.Saver()summ = tf.Summary()# Launch the graphcheckpoint_dir="./ckp" #NEED CHANGEwith tf.Session() as sess: writer = tf.summary.FileWriter(checkpoint_dir, sess.graph) if os.path.exists (checkpoint_dir): ckpt = tf.train.get_checkpoint_state(checkpoint_dir) saver.restore(sess, ckpt.model_checkpoint_path) else: sess.run(init) # Training cycle avg_cost = 0. i = 0 total = 0 batches = 0 binary_labels_list = np.array([]) data_list = np.array([]) for epoch in range(training_epochs): for video_level_record in os.listdir(video_level_record_path): video_level_record = os.path.join (video_level_record_path, video_level_record) for example in tf.python_io.tf_record_iterator(video_level_record): if i < batch_size: tf_example = tf.train.Example.FromString(example) #print (i, tf_example.features.feature['video_id'].bytes_list.value[0].decode(encoding='UTF-8')) labels = tf_example.features.feature['labels'].int64_list.value binary_labels = np.asarray([0] * n_classes) for label in labels: binary_labels[label] = 1 binary_labels = binary_labels.reshape(1, n_classes) binary_labels = np.asarray(binary_labels) data = np.asarray(tf_example.features.feature['mean_rgb'].float_list.value).reshape(1, n_input) if len(binary_labels_list) == 0: binary_labels_list = binary_labels binary_labels_list = np.vstack([binary_labels_list, binary_labels]) if len(data_list) == 0: data_list = data data_list = np.vstack([data_list, data]) i += 1 total += 1else: if len(data_list) > 0 and len(binary_labels_list) > 0: # Run optimization op (backprop) and cost op (to get loss value) _, c = sess.run([optimizer, cost], feed_dict={x: data_list, y: binary_labels_list}) avg_cost = c i = 0 binary_labels_list = np.array([]) data_list = np.array([]) batches += 1 if batches % display_step == 0: print("Epoch:", '%04d' % (epoch+1), \ "Iter:", "%d" % (batches), \ "cost=", str(avg_cost)) summ.value.add(tag="cost", simple_value = avg_cost) writer.add_summary (summ, batches) if batches % 100 == 0: save_path = saver.save(sess, checkpoint_dir + "/model.ckpt-" + str(batches)) print "saved", save_path print("Optimization Finished!") # Test model #correct_prediction = tf.equal(tf.argmax(pred, 1), tf.argmax(y, 1)) # Calculate accuracy #accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float")) #print("Accuracy:", accuracy.eval({x: mnist.test.images, y: mnist.test.labels}))
