如何使用训练有素的mod

Question

我已经训练了我的网络并保存了模型。到目前为止，我认为一切都是正确的。当我试图使用这个模型来预测独立程序中的数据值时，问题就开始了。我不想破坏训练模型，我只想用它来预测数值。我该如何恢复？我只发布了部分代码，可以看到一个整体there

def main(_):
    train_data_node = tf.placeholder(
        data_type(),
        shape=(BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))
    train_labels_node = tf.placeholder(tf.int64, shape=(BATCH_SIZE,))
    eval_data = tf.placeholder(
        data_type(),
        shape=(EVAL_BATCH_SIZE, IMAGE_SIZE, IMAGE_SIZE, NUM_CHANNELS))

    # The variables below hold all the trainable weights. They are passed an
    # initial value which will be assigned when we call:
    # {tf.global_variables_initializer().run()}
    conv1_weights = tf.Variable(
        tf.truncated_normal([5, 5, NUM_CHANNELS, 32],  # 5x5 filter, depth 32.
                            stddev=0.1,
                            seed=SEED, dtype=data_type()))
    conv1_biases = tf.Variable(tf.zeros([32], dtype=data_type()))
    conv2_weights = tf.Variable(tf.truncated_normal(
        [5, 5, 32, 64], stddev=0.1,
        seed=SEED, dtype=data_type()))
    conv2_biases = tf.Variable(tf.constant(0.1, shape=[64], dtype=data_type()))
    fc1_weights = tf.Variable(  # fully connected, depth 512.
        tf.truncated_normal([IMAGE_SIZE // 4 * IMAGE_SIZE // 4 * 64, 512],
                            stddev=0.1,
                            seed=SEED,
                            dtype=data_type()))
    fc1_biases = tf.Variable(tf.constant(0.1, shape=[512], dtype=data_type()))
    fc2_weights = tf.Variable(tf.truncated_normal([512, NUM_LABELS],
                                                  stddev=0.1,
                                                  seed=SEED,
                                                  dtype=data_type()))
    fc2_biases = tf.Variable(tf.constant(
        0.1, shape=[NUM_LABELS], dtype=data_type()))

    # We will replicate the model structure for the training subgraph, as well
    # as the evaluation subgraphs, while sharing the trainable parameters.
    def model(data, train=False):
        """The Model definition."""
        # 2D convolution, with 'SAME' padding (i.e. the output feature map has
        # the same size as the input). Note that {strides} is a 4D array whose
        # shape matches the data layout: [image index, y, x, depth].
        conv = tf.nn.conv2d(data,
                            conv1_weights,
                            strides=[1, 1, 1, 1],
                            padding='SAME')
        # Bias and rectified linear non-linearity.
        relu = tf.nn.relu(tf.nn.bias_add(conv, conv1_biases))
        # Max pooling. The kernel size spec {ksize} also follows the layout of
        # the data. Here we have a pooling window of 2, and a stride of 2.
        pool = tf.nn.max_pool(relu,
                              ksize=[1, 2, 2, 1],
                              strides=[1, 2, 2, 1],
                              padding='SAME')
        conv = tf.nn.conv2d(pool,
                            conv2_weights,
                            strides=[1, 1, 1, 1],
                            padding='SAME')
        relu = tf.nn.relu(tf.nn.bias_add(conv, conv2_biases))
        pool = tf.nn.max_pool(relu,
                              ksize=[1, 2, 2, 1],
                              strides=[1, 2, 2, 1],
                              padding='SAME')
        # Reshape the feature map cuboid into a 2D matrix to feed it to the
        # fully connected layers.
        pool_shape = pool.get_shape().as_list()
        reshape = tf.reshape(
            pool,
            [pool_shape[0], pool_shape[1] * pool_shape[2] * pool_shape[3]])
        # Fully connected layer. Note that the '+' operation automatically
        # broadcasts the biases.
        hidden = tf.nn.relu(tf.matmul(reshape, fc1_weights) + fc1_biases)
        # Add a 50% dropout during training only. Dropout also scales
        # activations such that no rescaling is needed at evaluation time.
        if train:
            hidden = tf.nn.dropout(hidden, 0.5, seed=SEED)
        return tf.matmul(hidden, fc2_weights) + fc2_biases

    # Training computation: logits + cross-entropy loss.
    logits = model(train_data_node, True)
    loss = tf.reduce_mean(tf.nn.sparse_softmax_cross_entropy_with_logits(
        labels=train_labels_node, logits=logits))

    # L2 regularization for the fully connected parameters.
    regularizers = (tf.nn.l2_loss(fc1_weights) + tf.nn.l2_loss(fc1_biases) +
                    tf.nn.l2_loss(fc2_weights) + tf.nn.l2_loss(fc2_biases))
    # Add the regularization term to the loss.
    loss += 5e-4 * regularizers

    # Optimizer: set up a variable that's incremented once per batch and
    # controls the learning rate decay.
    batch = tf.Variable(0, dtype=data_type())
    # Decay once per epoch, using an exponential schedule starting at 0.01.
    learning_rate = tf.train.exponential_decay(
        0.005,  # Base learning rate.
        batch * BATCH_SIZE,  # Current index into the dataset.
        train_size,  # Decay step.
        0.95,  # Decay rate.
        staircase=True)
    # Use simple momentum for the optimization.
    optimizer = tf.train.MomentumOptimizer(learning_rate,
                                           0.9).minimize(loss,
                                                         global_step=batch)

    # Predictions for the current training minibatch.
    train_prediction = tf.nn.softmax(logits)

    # Predictions for the test and validation, which we'll compute less often.
    eval_prediction = tf.nn.softmax(model(eval_data))

    # Small utility function to evaluate a dataset by feeding batches of data to
    # {eval_data} and pulling the results from {eval_predictions}.
    # Saves memory and enables this to run on smaller GPUs.
    def eval_in_batches(data, sess):
        """Get all predictions for a dataset by running it in small batches."""
        size = data.shape[0]
        if size < EVAL_BATCH_SIZE:
            raise ValueError("batch size for evals larger than dataset: %d" % size)
        predictions = numpy.ndarray(shape=(size, NUM_LABELS), dtype=numpy.float32)
        for begin in xrange(0, size, EVAL_BATCH_SIZE):
            end = begin + EVAL_BATCH_SIZE
            if end <= size:
                predictions[begin:end, :] = sess.run(
                    eval_prediction,
                    feed_dict={eval_data: data[begin:end, ...]})
            else:
                batch_predictions = sess.run(
                    eval_prediction,
                    feed_dict={eval_data: data[-EVAL_BATCH_SIZE:, ...]})
                predictions[begin:, :] = batch_predictions[begin - size:, :]
        return predictions

    # Create a local session to run the training.
    start_time = time.time()
    saver = tf.train.Saver()
    with tf.Session() as sess:
        # Run all the initializers to prepare the trainable parameters.
        tf.global_variables_initializer().run()
        print('Initialized!')
        # Loop through training steps.
        for step in xrange(int(num_epochs * train_size*4) // BATCH_SIZE):
            # Compute the offset of the current minibatch in the data.
            # Note that we could use better randomization across epochs.
            offset = (step * BATCH_SIZE) % (train_size - BATCH_SIZE)
            batch_data = train_data[offset:(offset + BATCH_SIZE), ...]
            batch_labels = train_labels[offset:(offset + BATCH_SIZE)]
            # This dictionary maps the batch data (as a numpy array) to the
            # node in the graph it should be fed to.
            feed_dict = {train_data_node: batch_data,
                         train_labels_node: batch_labels}
            # Run the optimizer to update weights.
            sess.run(optimizer, feed_dict=feed_dict)
            # print some extra information once reach the evaluation frequency
            if step % EVAL_FREQUENCY == 0:
                # fetch some extra nodes' data
                l, lr, predictions = sess.run([loss, learning_rate, train_prediction],
                                              feed_dict=feed_dict)
                elapsed_time = time.time() - start_time
                start_time = time.time()
                print('Step %d (epoch %.2f), %.1f ms' %
                      (step, float(step) * BATCH_SIZE / train_size,
                       1000 * elapsed_time / EVAL_FREQUENCY))
                print('Minibatch loss: %.3f, learning rate: %.6f' % (l, lr))
                print('Minibatch error: %.1f%%' % error_rate(predictions, batch_labels))
                sys.stdout.flush()
        # Finally print the result!
        test_error = error_rate(eval_in_batches(test_data, sess), test_labels)
        print('Test error: %.1f%%' % test_error)
        #saver.save(sess, '/my-model')
        save_path = saver.save(sess, "kupa/model_path")
        if FLAGS.self_test:
            print('test_error', test_error)
            assert test_error == 0.0, 'expected 0.0 test_error, got %.2f' % (
                test_error,)


if __name__ == '__main__':
  tf.app.run()