尝试使用课程中提供的框架Stanford cs231n，给定以下代码。
- 我可以看到准确率在不断提高，并且网络已经训练完毕，但在训练过程结束并检查验证集上的结果后，我该如何将一张图片输入到模型中并查看其预测结果呢？
我已经搜索了一番，但在tensorflow中没有找到像keras那样的内置预测函数。
初始化网络及其参数
# clear old variablestf.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 inX = 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_outy_out = simple_model(X,y)# define our losstotal_loss = tf.losses.hinge_loss(tf.one_hot(y,360),logits=y_out)mean_loss = tf.reduce_mean(total_loss)# define our optimizeroptimizer = tf.train.AdamOptimizer(5e-4) # select optimizer and set learning ratetrain_step = optimizer.minimize(mean_loss)用于评估模型的函数，无论是训练还是验证，并绘制结果：
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调用训练模型的函数
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)回答：
你不需要走得太远，只需将你的新（测试）特征矩阵X_test传入网络并执行前向传递 – 输出层就是预测结果。所以代码大致如下
session.run(y_out, feed_dict={X: X_test})