EEG_VGG_Model.py 文件源码

def Get_Pre_Trained_Weights(input_vars,name):
    with open("vgg16.tfmodel", mode='rb') as f:
        fileContent = f.read()

    graph_def = tf.GraphDef()
    graph_def.ParseFromString(fileContent)
    images = tf.placeholder(tf.float32,shape = (None, 64, 64, 3),name=name)
    tf.import_graph_def(graph_def, input_map={ "images": images })
    print "graph loaded from disk"

    graph = tf.get_default_graph()
    with tf.Session() as sess:
        init = tf.initialize_all_variables()
        sess.run(init)
        #batch = np.reshape(input_vars,(-1, 224, 224, 3))
        n_timewin = 7   
        convnets = []
        for i in xrange(n_timewin):
            feed_dict = { images:input_vars[:,i,:,:,:] }
            pool_tensor = graph.get_tensor_by_name("import/pool5:0")
            pool_tensor = sess.run(pool_tensor, feed_dict=feed_dict)
            convnets.append(tf.contrib.layers.flatten(pool_tensor))
        convpool = tf.pack(convnets, axis = 1)
        return convpool