我训练的时候看到准确率是90%以上,但是拿训练图片去预测,却都是一个类别
from keras.models import Model
from keras.layers import Input, Conv2D, GlobalAveragePooling2D, Dropout
from keras.layers import Activation, BatchNormalization, add, Reshape,DepthwiseConv2D,ReLU
from keras.applications.mobilenet import relu6, DepthwiseConv2D
from keras.utils.vis_utils import plot_model
from keras import backend as K
import cv2
import numpy as np
import time
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
def _conv_block(inputs, filters, kernel, strides):
"""Convolution Block
This function defines a 2D convolution operation with BN and relu6.
# Arguments
inputs: Tensor, input tensor of conv layer.
filters: Integer, the dimensionality of the output space.
kernel: An integer or tuple/list of 2 integers, specifying the
width and height of the 2D convolution window.
strides: An integer or tuple/list of 2 integers,
specifying the strides of the convolution along the width and height.
Can be a single integer to specify the same value for
all spatial dimensions.
# Returns
Output tensor.
"""
channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
x = Conv2D(filters, kernel, padding='same', strides=strides)(inputs)
x = BatchNormalization(axis=channel_axis)(x)
# return Activation(relu6)(x)
return Activation(ReLU(6.))(x)
def _bottleneck(inputs, filters, kernel, t, s, r=False):
"""Bottleneck
This function defines a basic bottleneck structure.
# Arguments
inputs: Tensor, input tensor of conv layer.
filters: Integer, the dimensionality of the output space.
kernel: An integer or tuple/list of 2 integers, specifying the
width and height of the 2D convolution window.
t: Integer, expansion factor.
t is always applied to the input size.
s: An integer or tuple/list of 2 integers,specifying the strides
of the convolution along the width and height.Can be a single
integer to specify the same value for all spatial dimensions.
r: Boolean, Whether to use the residuals.
# Returns
Output tensor.
"""
channel_axis = 1 if K.image_data_format() == 'channels_first' else -1
tchannel = K.int_shape(inputs)[channel_axis] * t
x = _conv_block(inputs, tchannel, (1, 1), (1, 1))
x = DepthwiseConv2D(kernel, strides=(s, s), depth_multiplier=1, padding='same')(x)
x = BatchNormalization(axis=channel_axis)(x)
# x = Activation(relu6)(x)
x = Activation(ReLU(6.))(x)
x = Conv2D(filters, (1, 1), strides=(1, 1), padding='same')(x)
x = BatchNormalization(axis=channel_axis)(x)
if r:
x = add([x, inputs])
return x
def _inverted_residual_block(inputs, filters, kernel, t, strides, n):
"""Inverted Residual Block
This function defines a sequence of 1 or more identical layers.
# Arguments
inputs: Tensor, input tensor of conv layer.
filters: Integer, the dimensionality of the output space.
kernel: An integer or tuple/list of 2 integers, specifying the
width and height of the 2D convolution window.
t: Integer, expansion factor.
t is always applied to the input size.
s: An integer or tuple/list of 2 integers,specifying the strides
of the convolution along the width and height.Can be a single
integer to specify the same value for all spatial dimensions.
n: Integer, layer repeat times.
# Returns
Output tensor.
"""
x = _bottleneck(inputs, filters, kernel, t, strides)
for i in range(1, n):
x = _bottleneck(x, filters, kernel, t, 1, True)
return x
def MobileNetv2( k=3):
"""MobileNetv2
This function defines a MobileNetv2 architectures.
# Arguments
input_shape: An integer or tuple/list of 3 integers, shape
of input tensor.
k: Integer, number of classes.
# Returns
MobileNetv2 model.
"""
inputs = Input(shape=(224, 224, 3), dtype='float32')
x = _conv_block(inputs, 32, (3, 3), strides=(2, 2))
x = _inverted_residual_block(x, 16, (3, 3), t=1, strides=1, n=1)
x = _inverted_residual_block(x, 24, (3, 3), t=6, strides=2, n=2)
x = _inverted_residual_block(x, 32, (3, 3), t=6, strides=2, n=3)
x = _inverted_residual_block(x, 64, (3, 3), t=6, strides=2, n=4)
x = _inverted_residual_block(x, 96, (3, 3), t=6, strides=1, n=3)
x = _inverted_residual_block(x, 160, (3, 3), t=6, strides=2, n=3)
x = _inverted_residual_block(x, 320, (3, 3), t=6, strides=1, n=1)
x = _conv_block(x, 1280, (1, 1), strides=(1, 1))
x = GlobalAveragePooling2D()(x)
x = Reshape((1, 1, 1280))(x)
x = Dropout(0.3, name='Dropout')(x)
x = Conv2D(k, (1, 1), padding='same')(x)
x = Activation('softmax', name='softmax')(x)
output = Reshape((k,))(x)
model = Model(inputs, output)
model.load_weights("model/ep052-loss0.066-val_loss0.730.h5")
return model
if name=="main":
model=MobileNetv2()
files=os.listdir("testimage3/")
for file in files:
print file
time1=time.time()
image = cv2.imread("testimage3/"+file)
image = cv2.resize(image, (224, 224))
image = image.transpose(1, 0, 2)
result = model.predict(np.array([image]),verbose=1)
print result
print "耗时:",time.time()-time1
我训练的时候看到准确率是90%以上,但是拿训练图片去预测,却都是一个类别
from keras.models import Model
from keras.layers import Input, Conv2D, GlobalAveragePooling2D, Dropout
from keras.layers import Activation, BatchNormalization, add, Reshape,DepthwiseConv2D,ReLU
from keras.applications.mobilenet import relu6, DepthwiseConv2D
from keras.utils.vis_utils import plot_model
from keras import backend as K
import cv2
import numpy as np
import time
import os
os.environ["CUDA_DEVICE_ORDER"] = "PCI_BUS_ID"
os.environ["CUDA_VISIBLE_DEVICES"] = "-1"
def _conv_block(inputs, filters, kernel, strides):
"""Convolution Block
This function defines a 2D convolution operation with BN and relu6.
def _bottleneck(inputs, filters, kernel, t, s, r=False):
"""Bottleneck
This function defines a basic bottleneck structure.
def _inverted_residual_block(inputs, filters, kernel, t, strides, n):
"""Inverted Residual Block
This function defines a sequence of 1 or more identical layers.
def MobileNetv2( k=3):
"""MobileNetv2
This function defines a MobileNetv2 architectures.
if name=="main":
model=MobileNetv2()
files=os.listdir("testimage3/")
for file in files:
print file
time1=time.time()
image = cv2.imread("testimage3/"+file)
image = cv2.resize(image, (224, 224))
image = image.transpose(1, 0, 2)
result = model.predict(np.array([image]),verbose=1)
print result
print "耗时:",time.time()-time1