Tensorflow:如何从训练的模型中预测单个图像？

Question

我是 tensorflow 新手，我正在尝试构建一个图像分类器。我已成功创建模型，并尝试在恢复模型后预测单个图像。我已经阅读了各种教程( https://github.com/sankit1/cv-tricks.com/blob/master/Tensorflow-tutorials/tutorial-2-image-classifier/predict.py )，但我无法弄清楚代码中的 feed-dict 内容。加载保存的模型后，我陷入了预测功能。有人可以帮助我并告诉我从保存的模型加载所有变量后该怎么做吗？

这是训练函数，它返回参数并将它们保存在模型中。

def trainModel(train, test, learning_rate=0.0001, num_epochs=2, minibatch_size=32, graph_filename='costs'):
"""
Implements a three-layer tensorflow neural network: LINEAR->RELU->LINEAR->RELU->LINEAR->SOFTMAX.

Input:
    train : training set
    test : test set
    learning_rate : learning rate 
    num_epochs : number of epochs 
    minibatch_size : size of minibatch
    print_cost : True to print the cost every epoch

Returns:
    parameters : parameters learnt by the model
"""

    ops.reset_default_graph() #for rerunning the model without resetting tf vars

# input and output shapes
    (n_x, m) = train.images.T.shape
    n_y = train.labels.T.shape[0]

    costs = [] #var for storing the costs for later use

    # create placeholders
    X, Y = placeholderCreator(n_x, n_y)

    parameters = paramInitializer()

    # Forward propagation
    Z3 = forwardPropagation(X, parameters)
    # Cost function
    cost = costCalc(Z3, Y)
    #Backpropagation using adam optimizer
    optimizer = tf.train.AdamOptimizer(learning_rate).minimize(cost)
    # Initialize tf variables
    init = tf.global_variables_initializer()
    minibatch_size = 32
    # Start session to compute Tensorflow graph
    with tf.Session() as sess:
    # Run initialization
        sess.run(init)

        for epoch in range(num_epochs): # Training loop
            epoch_cost = 0.
            num_minibatches = int(m / minibatch_size)
            for i in range(num_minibatches):
                minibatch_X, minibatch_Y = train.next_batch(minibatch_size)  # Get next batch of training data and labels
                _, minibatch_cost = sess.run([optimizer, cost], feed_dict={X: minibatch_X.T, Y: minibatch_Y.T}) # Execute optimizer and cost function
                epoch_cost += minibatch_cost / num_minibatches # Update epoch cost

        saver = tf.train.Saver()
        # Save parameters
        parameters = sess.run(parameters)
        saver.save(sess, "~/trained-model.ckpt")
        return parameters

这是我的预测函数，我试图预测图像。为了便于使用，我已将该图像转换为 MNIST 格式(predicting_data)。我加载保存的模型，对第三层的输出(最终输出)使用 softmax 函数。

def predict():

train = predicting_data.train
(n_x, m) = train.images.T.shape
n_y = train.labels.T.shape[0]
X, Y = placeholderCreator(n_x, n_y)
with tf.Session() as sess:
    new_saver = tf.train.import_meta_graph('~/trained-model.ckpt.meta')
    new_saver.restore(sess, '~/trained-model.ckpt')
    W1 = tf.get_default_graph().get_tensor_by_name('W1:0')
    b1 = tf.get_default_graph().get_tensor_by_name('b1:0')
    W2 = tf.get_default_graph().get_tensor_by_name('W2:0')
    b2 = tf.get_default_graph().get_tensor_by_name('b2:0')
    W3 = tf.get_default_graph().get_tensor_by_name('W3:0')
    b3 = tf.get_default_graph().get_tensor_by_name('b3:0')
    # forward propagation     
    Z1 = tf.add(tf.matmul(W1,X), b1)     
    A1 = tf.nn.relu(Z1)                  
    Z2 = tf.add(tf.matmul(W2,A1), b2)    
    A2 = tf.nn.relu(Z2)                  
    Z3 = tf.add(tf.matmul(W3,A2), b3) 
    y_pred = tf.nn.softmax(Z3) ####what to do after this????
    cost = sess.run(y_pred, feed_dict={X: train.images.T}) 

提前谢谢您!

Answer 1

正如维杰在评论中所说:

您的预测部分不正确，您需要使用get_tensor_by_name()函数从保存的图形中获取输入和预测张量，然后在您的sess.run

如果你看this post ，它涵盖了类似的问题并有一些代码示例。