softmax = model.predict(tf.expand_dims(image_batch[i], 0))

这段代码是使用 TensorFlow 框架中的模型（model）对一个图像数据（image_batch[i]）进行预测，其中 tf.expand_dims(image_batch[i], 0) 是在图像数据的第 0 个维度上添加了一个维度，使得图像数据的形状变成了 (1, height, width, channels)。这是因为 TensorFlow 中的模型需要输入一个 batch 的数据，而这里只有一张图像，需要通过添加一个维度来模拟一个 batch。预测结果是一个经过 softmax 函数处理后的概率分布，对应了各个类别的概率值。

相关问题

x_train, t_train, x_test, t_test = load_data('F:\\2023\\archive\\train') network = DeepConvNet() network.load_params("deep_convnet_params.pkl") print("calculating test accuracy ... ") sampled = 1000 x_test = x_test[:sampled] t_test = t_test[:sampled] prediect_result = [] for i in x_test: i = np.expand_dims(i, 0) y = network.predict(i) _result = network.predict(i) _result = softmax(_result) result = np.argmax(_result) prediect_result.append(int(result)) acc_number = 0 err_number = 0 for i in range(len(prediect_result)): if prediect_result[i] == t_test[i]: acc_number += 1 else: err_number += 1 print("预测正确数：", acc_number) print("预测错误数：", err_number) print("预测总数：", x_test.shape[0]) print("预测正确率：", acc_number / x_test.shape[0]) classified_ids = [] acc = 0.0 batch_size = 100 for i in range(int(x_test.shape[0] / batch_size)): tx = x_test[i * batch_size:(i + 1) * batch_size] tt = t_test[i * batch_size:(i + 1) * batch_size] y = network.predict(tx, train_flg=False) y = np.argmax(y, axis=1) classified_ids.append(y) acc += np.sum(y == tt) acc = acc / x_test.shape[0] classified_ids = np.array(classified_ids) classified_ids = classified_ids.flatten() max_view = 20 current_view = 1 fig = plt.figure() fig.subplots_adjust(left=0, right=1, bottom=0, top=1, hspace=0.2, wspace=0.2) mis_pairs = {} for i, val in enumerate(classified_ids == t_test): if not val: ax = fig.add_subplot(4, 5, current_view, xticks=[], yticks=[]) ax.imshow(x_test[i].reshape(28, 28), cmap=plt.cm.gray_r, interpolation='nearest') mis_pairs[current_view] = (t_test[i], classified_ids[i]) current_view += 1 if current_view > max_view: break print("======= 错误预测结果展示 =======") print("{view index: (label, inference), ...}") print(mis_pairs) plt.show()

这段代码是一个深度卷积神经网络用于对手写数字图像进行分类的代码。首先，通过load_data函数加载训练数据和测试数据集，然后使用DeepConvNet()创建了一个深度卷积神经网络，并通过load_params函数加载了预训练的参数。接着，从测试集中选取了1000个样本进行测试，并用predict函数对每个样本进行预测，将预测结果存储在predict_result列表中。然后，计算预测的正确率，并将错误预测结果展示出来。最后，调用show函数展示错误预测结果。

把 yolov3.weights 文件转换成 keras 适用的 h5 文件

要将 YOLOv3 的权重文件转换为 Keras 可以使用的 .h5 文件，需要进行以下步骤：

1. 下载并安装 `keras` 和 `tensorflow` 库：

pip install keras tensorflow

2. 下载 YOLOv3 的权重文件 `yolov3.weights` 和配置文件 `yolov3.cfg`。

3. 使用 `yolo_weights_convert.py` 脚本将权重文件转换为 Keras 模型：

python yolo_weights_convert.py yolov3.cfg yolov3.weights model_data/yolo.h5

其中，`yolov3.cfg` 是 YOLOv3 的配置文件路径，`yolov3.weights` 是权重文件路径，`model_data/yolo.h5` 是转换后的 Keras 模型保存路径。

以下是 `yolo_weights_convert.py` 的代码：

import argparse
import numpy as np
import struct
import os

from keras.layers import Conv2D, Input, ZeroPadding2D, BatchNormalization, LeakyReLU, UpSampling2D
from keras.layers.merge import add, concatenate
from keras.models import Model
from keras.engine.topology import Layer
from keras import backend as K

class YoloLayer(Layer):
    def __init__(self, anchors, max_grid, batch_size, warmup_batches, ignore_thresh, grid_scale,
                 obj_scale, noobj_scale, xywh_scale, class_scale, **kwargs):
        self.ignore_thresh = ignore_thresh
        self.warmup_batches = warmup_batches
        self.anchors = anchors
        self.grid_scale = grid_scale
        self.obj_scale = obj_scale
        self.noobj_scale = noobj_scale
        self.xywh_scale = xywh_scale
        self.class_scale = class_scale
        self.batch_size = batch_size
        self.true_boxes = K.placeholder(shape=(self.batch_size, 1, 1, 1, 50, 4))
        super(YoloLayer, self).__init__(**kwargs)

    def build(self, input_shape):
        super(YoloLayer, self).build(input_shape)

    def get_grid_size(self, net_h, net_w):
        return net_h // 32, net_w // 32

    def call(self, x):
        input_image, y_pred, y_true = x
        self.net_h, self.net_w = input_image.shape.as_list()[1:3]
        self.grid_h, self.grid_w = self.get_grid_size(self.net_h, self.net_w)

        # adjust the shape of the y_predict [batch, grid_h, grid_w, 3, 4+1+80]
        y_pred = K.reshape(y_pred, (self.batch_size, self.grid_h, self.grid_w, 3, 4 + 1 + 80))

        # convert the coordinates to absolute coordinates
        box_xy = K.sigmoid(y_pred[..., :2])
        box_wh = K.exp(y_pred[..., 2:4])
        box_confidence = K.sigmoid(y_pred[..., 4:5])
        box_class_probs = K.softmax(y_pred[..., 5:])

        # adjust the shape of the y_true [batch, 50, 4+1]
        object_mask = y_true[..., 4:5]
        true_class_probs = y_true[..., 5:]

        # true_boxes[..., 0:2] = center, true_boxes[..., 2:4] = wh
        true_boxes = self.true_boxes[..., 0:4]  # shape=[batch, 50, 4]
        true_xy = true_boxes[..., 0:2] * [self.grid_w, self.grid_h]  # shape=[batch, 50, 2]
        true_wh = true_boxes[..., 2:4] * [self.net_w, self.net_h]  # shape=[batch, 50, 2]
        true_wh_half = true_wh / 2.
        true_mins = true_xy - true_wh_half
        true_maxes = true_xy + true_wh_half

        # calculate the Intersection Over Union (IOU)
        pred_xy = K.expand_dims(box_xy, 4)
        pred_wh = K.expand_dims(box_wh, 4)
        pred_wh_half = pred_wh / 2.
        pred_mins = pred_xy - pred_wh_half
        pred_maxes = pred_xy + pred_wh_half

        intersect_mins = K.maximum(pred_mins, true_mins)
        intersect_maxes = K.minimum(pred_maxes, true_maxes)
        intersect_wh = K.maximum(intersect_maxes - intersect_mins, 0.)
        intersect_areas = intersect_wh[..., 0] * intersect_wh[..., 1]
        pred_areas = pred_wh[..., 0] * pred_wh[..., 1]
        true_areas = true_wh[..., 0] * true_wh[..., 1]
        union_areas = pred_areas + true_areas - intersect_areas
        iou_scores = intersect_areas / union_areas

        # calculate the best IOU, set the object mask and update the class probabilities
        best_ious = K.max(iou_scores, axis=4)
        object_mask_bool = K.cast(best_ious >= self.ignore_thresh, K.dtype(best_ious))
        no_object_mask_bool = 1 - object_mask_bool
        no_object_loss = no_object_mask_bool * box_confidence
        no_object_loss = self.noobj_scale * K.mean(no_object_loss)

        true_box_class = true_class_probs * object_mask
        true_box_confidence = object_mask
        true_box_xy = true_boxes[..., 0:2] * [self.grid_w, self.grid_h] - pred_mins
        true_box_wh = K.log(true_boxes[..., 2:4] * [self.net_w, self.net_h] / pred_wh)
        true_box_wh = K.switch(object_mask, true_box_wh, K.zeros_like(true_box_wh))  # avoid log(0)=-inf
        true_box_xy = K.switch(object_mask, true_box_xy, K.zeros_like(true_box_xy))  # avoid log(0)=-inf
        box_loss_scale = 2 - true_boxes[..., 2:3] * true_boxes[..., 3:4]

        xy_loss = object_mask * box_loss_scale * K.binary_crossentropy(true_box_xy, box_xy)
        wh_loss = object_mask * box_loss_scale * 0.5 * K.square(true_box_wh - box_wh)
        confidence_loss = true_box_confidence * K.binary_crossentropy(box_confidence, true_box_confidence) \
                          + (1 - true_box_confidence) * K.binary_crossentropy(box_confidence, true_box_confidence) \
                          * no_object_mask_bool
        class_loss = object_mask * K.binary_crossentropy(true_box_class, box_class_probs)

        xy_loss = K.mean(K.sum(xy_loss, axis=[1, 2, 3, 4]))
        wh_loss = K.mean(K.sum(wh_loss, axis=[1, 2, 3, 4]))
        confidence_loss = K.mean(K.sum(confidence_loss, axis=[1, 2, 3, 4]))
        class_loss = K.mean(K.sum(class_loss, axis=[1, 2, 3, 4]))

        loss = self.grid_scale * (xy_loss + wh_loss) + confidence_loss * self.obj_scale + no_object_loss \
               + class_loss * self.class_scale

        # warm up training
        batch_no = K.cast(self.batch_size / 2, dtype=K.dtype(object_mask))
        warmup_steps = self.warmup_batches
        warmup_lr = batch_no / warmup_steps
        batch_no = K.cast(K.minimum(warmup_steps, batch_no), dtype=K.dtype(object_mask))
        lr = self.batch_size / (batch_no * warmup_steps)
        warmup_decay = (1 - batch_no / warmup_steps) ** 4
        lr = lr * (1 - warmup_decay) + warmup_lr * warmup_decay

        self.add_loss(loss)
        self.add_metric(loss, name='loss', aggregation='mean')
        self.add_metric(xy_loss, name='xy_loss', aggregation='mean')
        self.add_metric(wh_loss, name='wh_loss', aggregation='mean')
        self.add_metric(confidence_loss, name='confidence_loss', aggregation='mean')
        self.add_metric(class_loss, name='class_loss', aggregation='mean')
        self.add_metric(lr, name='lr', aggregation='mean')

        return y_pred

    def compute_output_shape(self, input_shape):
        return input_shape[1]

    def get_config(self):
        config = {
            'ignore_thresh': self.ignore_thresh,
            'warmup_batches': self.warmup_batches,
            'anchors': self.anchors,
            'grid_scale': self.grid_scale,
            'obj_scale': self.obj_scale,
            'noobj_scale': self.noobj_scale,
            'xywh_scale': self.xywh_scale,
            'class_scale': self.class_scale
        }
        base_config = super(YoloLayer, self).get_config()
        return dict(list(base_config.items()) + list(config.items()))

def _conv_block(inp, convs, skip=True):
    x = inp
    count = 0
    for conv in convs:
        if count == (len(convs) - 2) and skip:
            skip_connection = x
        count += 1
        if conv['stride'] > 1:
            x = ZeroPadding2D(((1, 0), (1, 0)))(x)  # unlike tensorflow darknet prefer left and top paddings
        x = Conv2D(conv['filter'],
                   conv['kernel'],
                   strides=conv['stride'],
                   padding='valid' if conv['stride'] > 1 else 'same',  # unlike tensorflow darknet prefer left and top paddings
                   name='conv_' + str(conv['layer_idx']),
                   use_bias=False if conv['bnorm'] else True)(x)
        if conv['bnorm']:
            x = BatchNormalization(epsilon=0.001, name='bnorm_' + str(conv['layer_idx']))(x)
        if conv['leaky']:
            x = LeakyReLU(alpha=0.1, name='leaky_' + str(conv['layer_idx']))(x)
    return add([skip_connection, x]) if skip else x

def make_yolov3_model():
    input_image = Input(shape=(None, None, 3))
    true_boxes = Input(shape=(1, 1, 1, 50, 4))
    # Layer  0 => 4
    x = _conv_block(input_image, [{'filter': 32, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                                  {'filter': 64, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                                  {'filter': 32, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                                  {'filter': 64, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer  5 => 8
    x = _conv_block(x, [{'filter': 128, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                         {'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer  9 => 11
    x = _conv_block(x, [{'filter': 64, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 128, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 12 => 15
    x = _conv_block(x, [{'filter': 256, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                         {'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 16 => 36
    for i in range(7):
        x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                             {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    skip_36 = x
    # Layer 37 => 40
    x = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                         {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 41 => 61
    for i in range(7):
        x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                             {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    skip_61 = x
    # Layer 62 => 65
    x = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 2, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 66 => 74
    for i in range(3):
        x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                             {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True}])
    # Layer 75 => 79
    x = _conv_block(x, [{'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True}])

    # Layer 80 => 82
    yolo_82 = _conv_block(x, [{'filter': 1024, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                               {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False}], skip=False)

    # Layer 83 => 86
    x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True}],
                    skip=False)
    x = UpSampling2D(2)(x)
    x = concatenate([x, skip_61])
    # Layer 87 => 91
    x = _conv_block(x, [{'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                         {'filter': 256, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True}], skip=False)

    # Layer 92 => 94
    yolo_94 = _conv_block(x, [{'filter': 512, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                               {'filter': 255, 'kernel': 1, 'stride': 1, 'bnorm': False, 'leaky': False}],
                          skip=False)

    # Layer 95 => 98
    x = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True}],
                    skip=False)
    x = UpSampling2D(2)(x)
    x = concatenate([x, skip_36])
    # Layer 99 => 106
    yolo_106 = _conv_block(x, [{'filter': 128, 'kernel': 1, 'stride': 1, 'bnorm': True, 'leaky': True},
                                {'filter': 256, 'kernel': 3, 'stride': 1, 'bnorm': True, 'leaky': True},
                                {'filter