我已经研究了一段时间了。我发现了很多文章；但没有人真正将 tensorflow 推论显示为纯推论。它总是“使用服务引擎”或使用预先编码/定义的图形。

问题出在这里:我有一台偶尔检查更新模型的设备。然后，需要加载该模型并通过该模型运行输入预测。

在喀拉拉邦，这很简单:建立模型；训练模型并调用model.predict()。在scikit中学习相同的东西。

我可以获取一个新模型并加载它；我可以打印出所有的砝码；但是我该如何推断呢？

加载模型和打印权重的代码:

    with tf.Session() as sess:
        new_saver = tf.train.import_meta_graph(MODEL_PATH + '.meta', clear_devices=True)
        new_saver.restore(sess, MODEL_PATH)
        for var in tf.trainable_variables():
            print(sess.run(var))

def inference(images):
    '''
    Portion of the compute graph that takes an input and converts it into a Y output
    '''
    with tf.variable_scope('Conv1') as scope:
        C_1_1 = ld.cnn_layer(images, (5, 5, 3, 32), (1, 1, 1, 1), scope, name_postfix='1')
        C_1_2 = ld.cnn_layer(C_1_1, (5, 5, 32, 32), (1, 1, 1, 1), scope, name_postfix='2')
        P_1 = ld.pool_layer(C_1_2, (1, 2, 2, 1), (1, 2, 2, 1), scope)
    with tf.variable_scope('Dense1') as scope:
        P_1 = tf.reshape(C_1_2, (CONSTANTS.BATCH_SIZE, -1))
        dim = P_1.get_shape()[1].value
        D_1 = ld.mlp_layer(P_1, dim, NUM_DENSE_NEURONS, scope, act_func=tf.nn.relu)
    with tf.variable_scope('Dense2') as scope:
        D_2 = ld.mlp_layer(D_1, NUM_DENSE_NEURONS, CONSTANTS.NUM_CLASSES, scope)
    H = tf.nn.softmax(D_2, name='prediction')
    return H

GRAPH = tf.Graph()
with GRAPH.as_default():
    examples, labels = Inputs.read_inputs(CONSTANTS.RecordPaths,
                                          batch_size=CONSTANTS.BATCH_SIZE,
                                          img_shape=CONSTANTS.IMAGE_SHAPE,
                                          num_threads=CONSTANTS.INPUT_PIPELINE_THREADS)
    examples = tf.reshape(examples, [CONSTANTS.BATCH_SIZE, CONSTANTS.IMAGE_SHAPE[0],
                                     CONSTANTS.IMAGE_SHAPE[1], CONSTANTS.IMAGE_SHAPE[2]])
    logits = Vgg3CIFAR10.inference(examples)
    loss = Vgg3CIFAR10.loss(logits, labels)
    OPTIMIZER = tf.train.AdamOptimizer(CONSTANTS.LEARNING_RATE)

MODEL_PATH = 'models/' + CONSTANTS.MODEL_NAME + '.model'

images = tf.placeholder(tf.float32, shape=(1, 32, 32, 3))

def run_inference():
    '''Runs inference against a loaded model'''
    with tf.Session() as sess:
        #sess.run(tf.global_variables_initializer())
        new_saver = tf.train.import_meta_graph(MODEL_PATH + '.meta', clear_devices=True)
        new_saver.restore(sess, MODEL_PATH)
        pred = tf.get_default_graph().get_operation_by_name('prediction')
        rand = np.random.rand(1, 32, 32, 3)
        print(rand)
        print(pred)
        print(sess.run(pred, feed_dict={images: rand}))
        print('done')

run_inference()

import tensorflow as tf
import CONSTANTS
import Vgg3CIFAR10
import numpy as np
from scipy import misc
import time

MODEL_PATH = 'models/' + CONSTANTS.MODEL_NAME + '.model'
imgs_bsdir = 'C:/data/cifar_10/train/'

images = tf.placeholder(tf.float32, shape=(1, 32, 32, 3))

logits = Vgg3CIFAR10.inference(images)

def run_inference():
'''Runs inference against a loaded model'''
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        new_saver = tf.train.import_meta_graph(MODEL_PATH + '.meta')#, import_scope='1', input_map={'input:0': images})
        new_saver.restore(sess, MODEL_PATH)
        pred = tf.get_default_graph().get_operation_by_name('prediction')
        enq = sess.graph.get_operation_by_name(enqueue_op)
        #tf.train.start_queue_runners(sess)
        print(rand)
        print(pred)
        print(enq)
        for i in range(1, 25):
            img = misc.imread(imgs_bsdir + str(i) + '.png').astype(np.float32) / 255.0
            img = img.reshape(1, 32, 32, 3)
            print(sess.run(logits, feed_dict={images : img}))
            time.sleep(3)
        print('done')

run_inference()

最佳答案

好了，这花了太多时间去弄清楚。所以这就是世界其他地方的答案。

快速提醒:我需要坚持一个可以动态加载和推断的模型，而无需了解其基本原理或工作原理。

步骤1 :将模型创建为类，并理想地使用接口(interface)定义

class Vgg3Model:

    NUM_DENSE_NEURONS = 50
    DENSE_RESHAPE = 32 * (CONSTANTS.IMAGE_SHAPE[0] // 2) * (CONSTANTS.IMAGE_SHAPE[1] // 2)

    def inference(self, images):
        '''
        Portion of the compute graph that takes an input and converts it into a Y output
        '''
        with tf.variable_scope('Conv1') as scope:
            C_1_1 = ld.cnn_layer(images, (5, 5, 3, 32), (1, 1, 1, 1), scope, name_postfix='1')
            C_1_2 = ld.cnn_layer(C_1_1, (5, 5, 32, 32), (1, 1, 1, 1), scope, name_postfix='2')
            P_1 = ld.pool_layer(C_1_2, (1, 2, 2, 1), (1, 2, 2, 1), scope)
        with tf.variable_scope('Dense1') as scope:
            P_1 = tf.reshape(P_1, (-1, self.DENSE_RESHAPE))
            dim = P_1.get_shape()[1].value
            D_1 = ld.mlp_layer(P_1, dim, self.NUM_DENSE_NEURONS, scope, act_func=tf.nn.relu)
        with tf.variable_scope('Dense2') as scope:
            D_2 = ld.mlp_layer(D_1, self.NUM_DENSE_NEURONS, CONSTANTS.NUM_CLASSES, scope)
        H = tf.nn.softmax(D_2, name='prediction')
        return H

    def loss(self, logits, labels):
        '''
        Adds Loss to all variables
        '''
        cross_entr = tf.nn.sparse_softmax_cross_entropy_with_logits(logits=logits, labels=labels)
        cross_entr = tf.reduce_mean(cross_entr)
        tf.summary.scalar('cost', cross_entr)
        tf.add_to_collection('losses', cross_entr)
        return tf.add_n(tf.get_collection('losses'), name='total_loss')

model = vgg3.Vgg3Model()

def create_sess_ops():
    '''
    Creates and returns operations needed for running
    a tensorflow training session
    '''
    GRAPH = tf.Graph()
    with GRAPH.as_default():
        examples, labels = Inputs.read_inputs(CONSTANTS.RecordPaths,
                                          batch_size=CONSTANTS.BATCH_SIZE,
                                          img_shape=CONSTANTS.IMAGE_SHAPE,
                                          num_threads=CONSTANTS.INPUT_PIPELINE_THREADS)
        examples = tf.reshape(examples, [-1, CONSTANTS.IMAGE_SHAPE[0],
                                     CONSTANTS.IMAGE_SHAPE[1], CONSTANTS.IMAGE_SHAPE[2]], name='infer/input')
        logits = model.inference(examples)
        loss = model.loss(logits, labels)
        OPTIMIZER = tf.train.AdamOptimizer(CONSTANTS.LEARNING_RATE)
        gradients = OPTIMIZER.compute_gradients(loss)
        apply_gradient_op = OPTIMIZER.apply_gradients(gradients)
        gradients_summary(gradients)
        summaries_op = tf.summary.merge_all()
        return [apply_gradient_op, summaries_op, loss, logits], GRAPH

def main():
    '''
    Run and Train CIFAR 10
    '''
    print('starting...')
    ops, GRAPH = create_sess_ops()
    total_duration = 0.0
    with tf.Session(graph=GRAPH) as SESSION:
        COORDINATOR = tf.train.Coordinator()
        THREADS = tf.train.start_queue_runners(SESSION, COORDINATOR)
        SESSION.run(tf.global_variables_initializer())
        SUMMARY_WRITER = tf.summary.FileWriter('Tensorboard/' + CONSTANTS.MODEL_NAME, graph=GRAPH)
        GRAPH_SAVER = tf.train.Saver()

        for EPOCH in range(CONSTANTS.EPOCHS):
            duration = 0
            error = 0.0
            start_time = time.time()
            for batch in range(CONSTANTS.MINI_BATCHES):
                _, summaries, cost_val, prediction = SESSION.run(ops)
                error += cost_val
            duration += time.time() - start_time
            total_duration += duration
            SUMMARY_WRITER.add_summary(summaries, EPOCH)
            print('Epoch %d: loss = %.2f (%.3f sec)' % (EPOCH, error, duration))
            if EPOCH == CONSTANTS.EPOCHS - 1 or error < 0.005:
                print(
                'Done training for %d epochs. (%.3f sec)' % (EPOCH, total_duration)
            )
                break
        GRAPH_SAVER.save(SESSION, 'models/' + CONSTANTS.MODEL_NAME + '.model')
        with open('models/' + CONSTANTS.MODEL_NAME + '.pkl', 'wb') as output:
            pickle.dump(model, output)
        COORDINATOR.request_stop()
        COORDINATOR.join(THREADS)

MODEL_PATH = 'models/' + CONSTANTS.MODEL_NAME + '.model'
imgs_bsdir = 'C:/data/cifar_10/train/'

images = tf.placeholder(tf.float32, shape=(1, 32, 32, 3))
with open('models/vgg3.pkl', 'rb') as model_in:
model = pickle.load(model_in)
logits = model.inference(images)

def run_inference():
    '''Runs inference against a loaded model'''
    with tf.Session() as sess:
        sess.run(tf.global_variables_initializer())
        new_saver = tf.train.Saver()
        new_saver.restore(sess, MODEL_PATH)
        print("Starting...")
        for i in range(20, 30):
            print(str(i) + '.png')
            img = misc.imread(imgs_bsdir + str(i) + '.png').astype(np.float32) / 255.0
            img = img.reshape(1, 32, 32, 3)
            pred = sess.run(logits, feed_dict={images : img})
            max_node = np.argmax(pred)
            print('predicted label: ' + str(max_node))
        print('done')

run_inference()

关于python - TensorFlow推论，我们在Stack Overflow上找到一个类似的问题： https://stackoverflow.com/questions/43708616/