我有以下代码试图使用MobileNetV2进行预测，但是预测结果并未提供预期的结果，而是提供了错误的预测结果，预期的输出应该是这样的 [(‘n02504458’, ‘African_elephant’, 0.5459417), (‘n01871265’, ‘tusker’, 0.28918085), (‘n02504013’, ‘Indian_elephant’, 0.08010819)]
from keras.applications.mobilenet_v2 import decode_predictions#from keras.applications.imagenet_utils import decode_predictionsfrom keras.applications.imagenet_utils import preprocess_inputdef relu6(x): return K.relu(x, max_value=6)def _conv_block(inputs, filters, kernel, strides, use_bias=True): channel_axis = 1 if K.image_data_format() == 'channels_first' else -1 if nlay < 0 or nlay > 16: basename = 'conv_%d' % (nlay + 1) else: basename = 'expanded_conv_%d_expand' % nlay x = Conv2D(filters, kernel, padding='same', strides=strides, name=basename, use_bias=use_bias) (inputs) x = BatchNormalization(axis=channel_axis, name=basename + '_batch_normalization')(x) return Activation(relu6, name=basename + '_activation')(x)def _bottleneck(inputs, filters, kernel, t, s, r=False): global nlay channel_axis = 1 if K.image_data_format() == 'channels_first' else -1 #Create expansions layer only if needed (expansion factor >1) if t > 1: tchannel = K.int_shape(inputs)[channel_axis] * t x = _conv_block(inputs, tchannel, (1, 1), (1, 1), use_bias=False) else: x = inputs x = DepthwiseConv2D(kernel, strides=(s, s), depth_multiplier=1, padding='same', name='expanded_conv_%d_depthwise' % nlay, use_bias=False)(x) x = BatchNormalization(axis=channel_axis, name='expanded_conv_%d_depthwise_batch_normalization' % nlay)(x) x = Activation(relu6, name='expanded_conv_%d_depthwise_activation' % nlay)(x) x = Conv2D(filters, (1, 1), strides=(1, 1), padding='same', name='expanded_conv_%d_project' % nlay, use_bias=False)(x) x = BatchNormalization(axis=channel_axis, name='expanded_conv_%d_project_batch_normalization' % nlay)(x) if r: x = add([x, inputs], name="expanded_conv_%d_add" % nlay) nlay += 1 return x def _inverted_residual_block(inputs, filters, kernel, t, strides, n): x = _bottleneck(inputs, filters, kernel, t, strides) for i in range(1, n): x = _bottleneck(x, filters, kernel, t, 1, True) return xdef roundup(n): x = (n + 6) // 8 return x * 8def MobileNetv2(input_shape, k, width_multiplier=1.0): global nlay nlay = -1 inputs = Input(shape=input_shape) x = _conv_block(inputs, roundup(int(32 * width_multiplier)), (3, 3), strides=(2, 2), use_bias=False) nlay += 1 fix = 0 if width_multiplier - 1.3 < 0.01: fix = -2 x = _inverted_residual_block(x, roundup(int(16 * width_multiplier)), (3, 3), t=1, strides=1, n=1) x = _inverted_residual_block(x, roundup(int(24 * width_multiplier)), (3, 3), t=6, strides=2, n=2) x = _inverted_residual_block(x, roundup(int(32 * width_multiplier)), (3, 3), t=6, strides=2, n=3) x = _inverted_residual_block(x, roundup(int(64 * width_multiplier) + fix), (3, 3), t=6, strides=2, n=4) x = _inverted_residual_block(x, roundup(int(96 * width_multiplier)), (3, 3), t=6, strides=1, n=3) x = _inverted_residual_block(x, roundup(int(160 * width_multiplier)), (3, 3), t=6, strides=2, n=3) x = _inverted_residual_block(x, roundup(int(320 * width_multiplier)), (3, 3), t=6, strides=1, n=1) last_conv_size = max(1280, int(1280 * width_multiplier)) x = _conv_block(x, last_conv_size, (1, 1), strides=(1, 1), use_bias=False) x = GlobalAveragePooling2D()(x) x = Reshape((1, 1, last_conv_size))(x) x = Dropout(0.3, name='Dropout')(x) x = Conv2D(k, (1, 1), padding='same', name='logits', use_bias=True)(x) x = Activation('softmax', name='softmax')(x) output = Reshape((k,), name='out')(x) model = Model(inputs, output) plot_model(model, to_file='MobileNetv2.png', show_shapes=True) return model if __name__ == '__main__': model = MobileNetv2((224, 224, 3), 1000) img_path = 'elephant.jpg' img = image.load_img(img_path, target_size=(224, 224)) x = image.img_to_array(img) x = np.expand_dims(x, axis=0) x = preprocess_input(x) print('Input image shape:', x.shape) preds = model.predict(x) print(':', decode_predictions(preds))预测结果输出
[[(‘n15075141’, ‘toilet_tissue’, 0.001), (‘n02319095’, ‘sea_urchin’, 0.001), (‘n02395406’, ‘hog’, 0.001), (‘n02391049’, ‘zebra’, 0.001), (‘n02389026’, ‘sorrel’, 0.001)]]
回答：
您尚未训练您的网络。您可以选择以下方法之一：
-
加载预训练的权重。这种选项仅在您使用预训练的Keras网络时可用。对于图像分类，一个不错的选择是ImageNet：
model = ResNet50(weights='imagenet') -
使用
model.fit方法在某些数据集上训练您的网络。这种方法也可以用于自定义网络。
