Question

我试图获得训练集的预测，但我总是得到每张图像相同的结果。有人可以帮帮我吗？

这就是我定义网络的方式：

x = tf.placeholder(tf.float32, shape=[None, image_size_h, image_size_v, num_channels])
y_ = tf.placeholder(tf.float32, shape=[None, num_classes])
keep_prob = tf.placeholder(tf.float32)

def conv_net(x1, weights, biases):
    # First Convolution Layer
    conv1 = conv2d_pool(x1, weights['wc1'], biases['bc1'], 'conv1')
    #print(conv1.get_shape())

    # Second Convolution Layer
    conv2 = conv2d(conv1, weights['wc2'], biases['bc2'], 'conv2')
    conv3 = conv2d(conv2, weights['wc3'], biases['bc3'], 'conv3')
    conv4 = conv2d(conv3, weights['wc4'], biases['bc4'],'conv4')
    conv5 = conv2d(conv4, weights['wc5'], biases['bc5'],'conv5')
    conv6 = conv2d(conv5, weights['wc6'], biases['bc6'],'conv6')
    # Fully connected layer
    fc1 = fullycon(conv6, weights['wd1'], biases['bd1'], 'fc1')
    # Dropout
    fc1_drop = tf.nn.dropout(fc1, keep_prob)
    fc2 = fullytanh(fc1_drop, weights['wd2'], biases['bd2'], 'fc2')
    out = tf.add(tf.matmul(fc2, weights['out']), biases['bout'])
    return out
pred = conv_net(x, weights, biases)

prediyo = tf.argmax(tf.nn.softmax(pred), axis=1)
# Loss and optimizer

correct_prediction = tf.equal(tf.argmax(y_,axis=1), tf.argmax(pred,axis=1))

accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))
optimizer = tf.train.AdamOptimizer(learning_rate=lr).minimize(cost)

虽然我做了预测：

result_boxes_n = normalise_images(result_boxes)

for n in range (0,result_boxes.shape[0]):

    predicted = prediyo.eval(feed_dict={x:np.reshape(result_boxes_n[n,:,:,:],(1,9,8,3)), keep_prob: 1})
    print(predicted)

尽管训练精度高于90％，但预测的输出始终是同一级别。但是，如果我这样做：

prediyo.eval(feed_dict={x:result_boxes_n, keep_prob: 1})

它有效，但当我尝试做个别预测时没有。

提前致谢

获得高训练准确性但对训练集的错误预测

0 个答案: