Question

考虑到以下代码，尝试使用课程Stanford cs231n中提供的框架。

我可以看到准确性越来越好并且网络经过训练但是在训练过程之后并在验证集上检查结果，我将如何将一个图像输入到模型中并查看其预测？我在tensorflow找到了keras中的内置预测函数，但# clear old variables tf.reset_default_graph() # setup input (e.g. the data that changes every batch) # The first dim is None, and gets sets automatically based on batch size fed in X = tf.placeholder(tf.float32, [None, 30, 30, 1]) y = tf.placeholder(tf.int64, [None]) is_training = tf.placeholder(tf.bool) def simple_model(X,y): # define our weights (e.g. init_two_layer_convnet) # setup variables Wconv1 = tf.get_variable("Wconv1", shape=[7, 7, 1, 32]) # Filter of size 7x7 with depth of 3. No. of filters is 32 bconv1 = tf.get_variable("bconv1", shape=[32]) W1 = tf.get_variable("W1", shape=[4608, 360]) # 5408 is 13x13x32 where 13x13 is the output of 7x7 filter on 32x32 image with padding of 2. b1 = tf.get_variable("b1", shape=[360]) # define our graph (e.g. two_layer_convnet) a1 = tf.nn.conv2d(X, Wconv1, strides=[1,2,2,1], padding='VALID') + bconv1 h1 = tf.nn.relu(a1) h1_flat = tf.reshape(h1,[-1,4608]) y_out = tf.matmul(h1_flat,W1) + b1 return y_out y_out = simple_model(X,y) # define our loss total_loss = tf.losses.hinge_loss(tf.one_hot(y,360),logits=y_out) mean_loss = tf.reduce_mean(total_loss) # define our optimizer optimizer = tf.train.AdamOptimizer(5e-4) # select optimizer and set learning rate train_step = optimizer.minimize(mean_loss)中找不到内置的预测函数。

初始化网络及其参数

def run_model(session, predict, loss_val, Xd, yd,
              epochs=1, batch_size=64, print_every=100,
              training=None, plot_losses=False):
    # Have tensorflow compute accuracy
    correct_prediction = tf.equal(tf.argmax(predict,1), y)
    accuracy = tf.reduce_mean(tf.cast(correct_prediction, tf.float32))

    # shuffle indicies
    train_indicies = np.arange(Xd.shape[0])
    np.random.shuffle(train_indicies)

    training_now = training is not None

    # setting up variables we want to compute and optimize
    # if we have a training function, add that to things we compute
    variables = [mean_loss,correct_prediction,accuracy]
    if training_now:
        variables[-1] = training

    # counter 
    iter_cnt = 0
    for e in range(epochs):
        # keep track of losses and accuracy
        correct = 0
        losses = []
        # make sure we iterate over the dataset once
        for i in range(int(math.ceil(Xd.shape[0]/batch_size))):
            # generate indicies for the batch
            start_idx = (i*batch_size)%Xd.shape[0]
            idx = train_indicies[start_idx:start_idx+batch_size]

            # create a feed dictionary for this batch
            feed_dict = {X: Xd[idx,:],
                         y: yd[idx],
                         is_training: training_now }
            # get batch size
            actual_batch_size = yd[idx].shape[0]

            # have tensorflow compute loss and correct predictions
            # and (if given) perform a training step
            loss, corr, _ = session.run(variables,feed_dict=feed_dict)

            # aggregate performance stats
            losses.append(loss*actual_batch_size)
            correct += np.sum(corr)

            # print every now and then
            if training_now and (iter_cnt % print_every) == 0:
                print("Iteration {0}: with minibatch training loss = {1:.3g} and accuracy of {2:.2g}"\
                      .format(iter_cnt,loss,np.sum(corr)/actual_batch_size))
            iter_cnt += 1
        total_correct = correct/Xd.shape[0]
        total_loss = np.sum(losses)/Xd.shape[0]
        print("Epoch {2}, Overall loss = {0:.3g} and accuracy of {1:.3g}"\
              .format(total_loss,total_correct,e+1))
        if plot_losses:
            plt.plot(losses)
            plt.grid(True)
            plt.title('Epoch {} Loss'.format(e+1))
            plt.xlabel('minibatch number')
            plt.ylabel('minibatch loss')
            plt.show()
    return total_loss,total_correct

评估模型是否用于训练或验证并绘制结果的功能：

init = tf.global_variables_initializer()
with tf.Session() as sess:
    sess.run(init)
    print('Training')
    run_model(sess,y_out,mean_loss,x_train,y_train,1,64,100,train_step,True)
    print('Validation')
    run_model(sess,y_out,mean_loss,x_val,y_val,1,64)

训练模型的函数调用

Install git if not available via apt-get (Ubuntu) or yum install (CentOS)
$ mkdir test-repo.git
$ cd test-repo.git
$ git init --bare

Answer 1

您不需要走得太远，只需将新的（测试）特征矩阵Message::with(['conversationLatestFirst']) ->where(.....) ->get()传递到网络中并执行正向传递 - 输出层就是预测。所以代码是这样的

X_test

TF |如何在训练结束后从CNN预测

1 个答案: