我的深层网络在CNN训练时什么也学不到

Question

为了训练一个CNN，我有一个5（批量大小）的输入，尺寸为16x12x1，我使用的是 conv1->；maxpool->；conv2->；maxpool->；完全连接->；softmax。 conv1:5x5x1，16个过滤器。 conv2:5x5x16，16个过滤器。 fc:16*4*3，64个隐藏节点。 softmax:8个输出

我的网络什么都学不到。它也没有显示任何错误。 成本函数与2.05xxx一致 可能的原因是什么？ 测试的准确度为0.2，有时为0.0。在

下面是示例代码：

layer1_weights = tf.Variable(tf.random_normal( [5,5,1,16], stddev=0.01))
layer1_biases  = tf.Variable(tf.zeros([16]))

layer2_weights = tf.Variable(tf.random_normal( [5,5,16,16], stddev=0.01))
layer2_biases  = tf.Variable(tf.zeros([16]))

fc_weights     = tf.Variable(tf.truncated_normal([16*4*3, 64], stddev=0.01))
fc_biases      = tf.Variable(tf.constant(1.0, shape=[64]))

Softmax_weights= tf.Variable(tf.truncated_normal([64, 8], stddev=0.01))
Softmax_biases = tf.Variable(tf.constant(1.0, shape=[8]))

def mycnn(x):
    conv1 = tf.nn.relu(tf.add(tf.nn.conv2d(x, layer1_weight, strides=[1, 1, 1, 1], padding='SAME'), layer1_biases))
    layer1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    conv2 = tf.nn.relu(tf.add(tf.nn.conv2d(layer1, layer2_weights, strides=[1, 1, 1, 1], padding='SAME'), layer2_biases))
    layer2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME')
    shape = layer2.get_shape().as_list()
    reshape = tf.reshape(layer2, [shape[0], shape[1] * shape[2] * shape[3]])
    FClayer = tf.nn.relu(tf.add(tf.matmul(reshape, model.fc_weights), model.fc_biases))
    softmax_layer = tf.add(tf.matmul(FClayer, model.Softmax_weights), model.Softmax_biases)
    return softmax_layer

def reshapedata(data_train,data_test,model):
   data_train = np.reshape(data_train,[-1,16,12,1])
   data_test  = np.reshape(data_test,[-1, 16,12,1])
return data_train,data_test

def batchdata(data,label, batchsize):
    # generate random number required to batch data
    order_num = random.sample(range(1, len(data)), batchsize)
    data_batch = []
    label_batch = []
    for i in range(len(order_num)):
        data_batch.append(data[order_num[i-1]])
        label_batch.append(label[order_num[i-1]])
    return data_batch, label_batch

sess = tf.Session()
data_train, data_test, label_train, label_test = load_data(Datainfo, ClassInfo, folder)
data_train, data_test, = reshapedata(data_train, data_test, model)
# input output placeholders
x  = tf.placeholder(tf.float32, [5, 16,12,1]) 
y_ = tf.placeholder(tf.float32, [5, 8])
y  = mycnn(x)
# loss
cost = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(y, y_))
# train step
train_step = tf.train.AdamOptimizer(1e-3).minimize(cost)
correct_prediction = tf.equal(tf.argmax(y,1), tf.argmax(y_,1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

sess.run(tf.global_variables_initializer())

for i in range(STEPS):
    image_batch, label_batch = batchdata(data_train, label_train, model.BATCH_SIZE)

    c = sess.run( cost, feed_dict={x: image_batch, y_: label_batch})
    print('Epoch',i,'completed out of',STEPS,'loss:',c )
    sess.run(train_step, feed_dict={ x: image_batch, y_: label_batch})

我的成本函数

^{pr2}$

更新

我在网络FClayer = tf.nn.dropout(FClayer,0.8)中又增加了一条线路，我的成本函数显示出某种趋势，但并不平滑。 当我添加代码段时发生了什么？对平滑成本函数有什么建议吗

图表：x轴-迭代次数，y轴-成本

当我使用0.6的偏差值时，测试样本的准确度为63%，而测试样本的0.8和1.0的准确度分别为80%和40%。在