基于股票历史数据实现股票价格预测的Python仿真源码+数据（课程设计）.zip

import tensorflow as tf slim = tf.contrib.slim import readcifar10 import os def model_fn_v1(net,keep_prob=0.5, is_training = True): batch_norm_params = { 'is_training': is_training, 'decay': 0.997, 'epsilon': 1e-5, 'scale': True, 'updates_collections': tf.GraphKeys.UPDATE_OPS, } endpoints = {} with slim.arg_scope( [slim.conv2d], weights_regularizer=slim.l2_regularizer(0.0001), weights_initializer=slim.variance_scaling_initializer(), activation_fn=tf.nn.relu, normalizer_fn=slim.batch_norm, normalizer_params=batch_norm_params): with slim.arg_scope([slim.batch_norm], **batch_norm_params): with slim.arg_scope([slim.max_pool2d], padding='SAME') as arg_sc: net = slim.conv2d(net, 32, [3, 3], activation_fn=None, normalizer_fn=None, scope='conv1') net = slim.conv2d(net, 32, [3, 3], activation_fn=None, normalizer_fn=None, scope='conv2') endpoints["conv2"] = net net = slim.max_pool2d(net, [3, 3], stride=2, scope="pool1") net = slim.conv2d(net, 64, [3, 3], activation_fn=None, normalizer_fn=None, scope='conv3') net = slim.conv2d(net, 64, [3, 3], activation_fn=None, normalizer_fn=None, scope='conv4') endpoints["conv4"] = net net = slim.max_pool2d(net, [3, 3], stride=2, scope="pool2") net = slim.conv2d(net, 128, [3, 3], activation_fn=None, normalizer_fn=None, scope='conv5') net = slim.conv2d(net, 128, [3, 3], activation_fn=None, normalizer_fn=None, scope='conv6') net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True) net = slim.flatten(net) net = slim.dropout(net, keep_prob, scope='dropout1') net = slim.fully_connected(net, 10, activation_fn=None, scope='fc2') endpoints["fc"] = net return net def resnet_blockneck(net, kernel_size, down, stride, is_training): batch_norm_params = { 'is_training': is_training, 'decay': 0.997, 'epsilon': 1e-5, 'scale': True, 'updates_collections': tf.GraphKeys.UPDATE_OPS, } shortcut = net with slim.arg_scope( [slim.conv2d], weights_regularizer=slim.l2_regularizer(0.0001), weights_initializer=slim.variance_scaling_initializer(), activation_fn=tf.nn.relu, normalizer_fn=slim.batch_norm, normalizer_params=batch_norm_params): with slim.arg_scope([slim.batch_norm], **batch_norm_params): with slim.arg_scope([slim.conv2d, slim.max_pool2d], padding='SAME') as arg_sc: if kernel_size != net.get_shape().as_list()[-1]: shortcut = slim.conv2d(net, kernel_size, [1, 1]) if stride != 1: shortcut = slim.max_pool2d(shortcut, [3, 3], stride=stride, scope="pool1") net = slim.conv2d(net, kernel_size // down, [1, 1]) net = slim.conv2d(net, kernel_size // down, [3, 3]) if stride != 1: net = slim.max_pool2d(net, [3, 3], stride=stride, scope="pool1") net = slim.conv2d(net, kernel_size, [1, 1]) net = net + shortcut return net def model_fn_resnet(net, keep_prob=0.5, is_training = True): with slim.arg_scope([slim.conv2d, slim.max_pool2d], padding='SAME') as arg_sc: net = slim.conv2d(net, 64, [3, 3], activation_fn=tf.nn.relu) net = slim.conv2d(net, 64, [3, 3], activation_fn=tf.nn.relu) net = resnet_blockneck(net, 128, 4, 2, is_training) net = resnet_blockneck(net, 128, 4, 1, is_training) net = resnet_blockneck(net, 256, 4, 2, is_training) net = resnet_blockneck(net, 256, 4, 1, is_training) net = resnet_blockneck(net, 512, 4, 2, is_training) net = resnet_blockneck(net, 512, 4, 1, is_training) #net = tf.reduce_mean(net, [1, 2], name='pool5', keep_dims=True) net = slim.flatten(net) net = slim.fully_connected(net, 1024, activation_fn=tf.nn.relu, scope='fc1') net = slim.dropout(net, keep_prob, scope='dropout1') net = slim.fully_connected(net, 10, activation_fn=None, scope='fc2') return net def model(image, keep_prob=0.5, is_training=True): batch_norm_params = { "is_training": is_training, "epsilon": 1e-5, "decay": 0.997, 'scale': True, 'updates_collections': tf.GraphKeys.UPDATE_OPS } with slim.arg_scope( [slim.conv2d], weights_initializer=slim.variance_scaling_initializer(), activation_fn=tf.nn.relu, weights_regularizer=slim.l2_regularizer(0.0001), normalizer_fn=slim.batch_norm, normalizer_params=batch_norm_params): net = slim.conv2d(image, 32, [3, 3], scope='conv1') net = slim.conv2d(net, 32, [3, 3], scope='conv2') net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool1') net = slim.conv2d(net, 64, [3, 3], scope='conv3') net = slim.conv2d(net, 64, [3, 3], scope='conv4') net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool2') net = slim.conv2d(net, 128, [3, 3], scope='conv5') net = slim.conv2d(net, 128, [3, 3], scope='conv6') net = slim.max_pool2d(net, [3, 3], stride=2, scope='pool3') net = slim.conv2d(net, 256, [3, 3], scope='conv7') net = tf.reduce_mean(net, axis=[1, 2]) # nhwc--n11c net = slim.flatten(net) net = slim.fully_connected(net, 1024) net = slim.dropout(net, keep_prob) net = slim.fully_connected(net, 10) return net # 10 dim vec def func_optimal(loss_val): with tf.variable_scope("optimizer"): global_step = tf.Variable(0, trainable=False) lr = tf.train.exponential_decay(0.0001, global_step, decay_steps=10000, decay_rate=0.99, staircase=True) # ##更新 BN update_ops = tf.get_collection(tf.GraphKeys.UPDATE_OPS) with tf.control_dependencies(update_ops): optimizer = tf.train.AdamOptimizer(lr).minimize(loss_val, global_step) return optimizer, global_step, lr def loss(logist, label): one_hot_label = slim.one_hot_encoding(label, 10) slim.losses.softmax_cross_entropy(logist, one_hot_label) reg_set = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) l2_loss = tf.add_n(reg_set) slim.losses.add_loss(l2_loss) totalloss = slim.losses.get_total_loss() return totalloss, l2_loss def train_net(): batchsize = 128 floder_name = "logdirs" no_data = 1 if not os.path.exists(floder_name): os.mkdir(floder_name) images_train, labels_train = readcifar10.read_from_tfrecord_v1(batchsize, 0, no_data) images_test, labels_test = readcifar10.read_from_tfrecord_v1(batchsize, 1) input_data = tf.placeholder(tf.float32, shape=[None, 32, 32, 3], name="input_224") input_label = tf.placeholder(tf.int64, shape=[None], name="input_label") is_training = tf.placeholder(tf.bool, shape=None, name = "is_training") keep_prob = tf.placeholder(tf.float32, shape=None, name= "keep_prob") logits = model(input_data, keep_prob=keep_prob) softmax = tf.nn.softmax(logits) pred_max = tf.argmax(softmax, 1) correct_pred = tf.equal(input_label, pred_max) accurancy = tf.reduce_mean(tf.cast(correct_pred, tf.float32)) total_loss, l2_loss = loss(logits, input_label) # one_hot_labels = slim.one_hot_encoding(input_label, 10) # slim.losses.softmax_cross_entropy(logits, one_hot_labels) # # reg_set = tf.get_collection(tf.GraphKeys.REGULARIZATION_LOSSES) # l2_loss = tf.a