def densenet121_model(img_rows, img_cols, color_type=1, nb_dense_block=4, growth_rate=48, nb_filter=64, reduction=0.5, dropout_rate=0.0, weight_decay=1e-4, num_classes=None):
'''
DenseNet 121 Model for Keras
Model Schema is based on
https://github.com/flyyufelix/DenseNet-Keras
ImageNet Pretrained Weights
Theano: https://drive.google.com/open?id=0Byy2AcGyEVxfMlRYb3YzV210VzQ
TensorFlow: https://drive.google.com/open?id=0Byy2AcGyEVxfSTA4SHJVOHNuTXc
# Arguments
nb_dense_block: number of dense blocks to add to end
growth_rate: number of filters to add per dense block
nb_filter: initial number of filters
reduction: reduction factor of transition blocks.
dropout_rate: dropout rate
weight_decay: weight decay factor
classes: optional number of classes to classify images
weights_path: path to pre-trained weights
# Returns
A Keras model instance.
'''
eps = 1.1e-5
# compute compression factor
compression = 1.0 - reduction
# Handle Dimension Ordering for different backends
global concat_axis
if K.image_dim_ordering() == 'tf':
concat_axis = 3
img_input = Input(shape=(img_rows, img_cols, color_type), name='data')
else:
concat_axis = 1
img_input = Input(shape=(color_type, img_rows, img_cols), name='data')
# From architecture for ImageNet (Table 1 in the paper)
nb_filter = 64
nb_layers = [6,12,24,16] # For DenseNet-121
nb_layers = [1,2,3,2]
# Initial convolution
print('********************',img_input.shape)
x = ZeroPadding2D((3, 3), name='conv1_zeropadding')(img_input)
print('********************',x.shape)
x = Convolution2D(nb_filter, 7, 7, subsample=(2, 2), name='conv1', bias=False)(x)
print('********************',x.shape)
x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv1_bn')(x)
print('********************',x.shape)
x = Scale(axis=concat_axis, name='conv1_scale')(x)
print('********************',x.shape)
x = Activation('relu', name='relu1')(x)
print('********************',x.shape)
x = ZeroPadding2D((1, 1), name='pool1_zeropadding')(x)
print('********************',x.shape)
x = MaxPooling2D((3, 3), strides=(2, 2), name='pool1')(x)
print('********************',x.shape)
# Add dense blocks
for block_idx in range(nb_dense_block - 1):
stage = block_idx+2
x, nb_filter = dense_block(x, stage, nb_layers[block_idx], nb_filter, growth_rate, dropout_rate=dropout_rate, weight_decay=weight_decay)
print('********************',x.shape)
# Add transition_block
x = transition_block(x, stage, nb_filter, compression=compression, dropout_rate=dropout_rate, weight_decay=weight_decay)
nb_filter = int(nb_filter * compression)
final_stage = stage + 1
x, nb_filter = dense_block(x, final_stage, nb_layers[-1], nb_filter, growth_rate, dropout_rate=dropout_rate, weight_decay=weight_decay)
x = BatchNormalization(epsilon=eps, axis=concat_axis, name='conv'+str(final_stage)+'_blk_bn')(x)
x = Scale(axis=concat_axis, name='conv'+str(final_stage)+'_blk_scale')(x)
x = Activation('tanh', name='tanh'+str(final_stage)+'_blk')(x)
#x_fc = GlobalAveragePooling2D(name='pool'+str(final_stage))(x)
print('/',x.shape)
x_fc = Conv2D(500, kernel_size=(7,7), activation='elu', strides=(1,1), padding='valid', name='conv3',
kernel_regularizer=regularizers.l2(0.01))(x)
print('hhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhhh',x_fc.shape)
#x_fc = Dense(1000, name='fc6')(x_fc)
x_fc = Activation('softmax', name='prob')(x_fc)
model = Model(img_input, x_fc, name='densenet')
## if K.image_dim_ordering() == 'th':
## # Use pre-trained weights for Theano backend
## weights_path = '/home/xm/桌面/云量检测/cnn_finetune-master/imagenet_models/imagenet_models/densenet121_weights_tf.h5'
## else:
## # Use pre-trained weights for Tensorflow backend
## weights_path = '/home/xm/桌面/云量检测/cnn_finetune-master/imagenet_models/imagenet_models/densenet121_weights_tf.h5'
## model.load_weights(weights_path, by_name=True)
# Truncate and replace softmax layer for transfer learning
# Cannot use model.layers.pop() since model is not of Sequential() type
# The method below works since pre-trained weights are stored in layers but not in the model
x_newfc = GlobalAveragePooling2D(name='pool'+str(final_stage))(x)
x_newfc = Dense(num_classes, name='fc6')(x_newfc)
x_newfc = Activation('softmax', name='prob')(x_newfc)
model = Model(img_input, x_newfc)
# Learning rate is changed to 0.001
sgd = SGD(lr=1e-3, decay=1e-6, momentum=0.9, nesterov=True)
model.compile(optimizer=sgd, loss='categorical_crossentropy', metrics=['accuracy'])
print("Training........................................")
## list_train_loss = []
## list_test_loss = []
## list_learning_rate = []
##
## for e in range(nb_epoch):
##
## if e == int(0.5 * nb_epoch):
## K.set_value(model.optimizer.lr, np.float32(learning_rate / 10.))
##
## if e == int(0.75 * nb_epoch):
## K.set_value(model.optimizer.lr, np.float32(learning_rate / 100.))
##
## split_size = batch_size
## num_splits = X_train.shape[0] / split_size
## arr_splits = np.array_split(np.arange(X_train.shape[0]), num_splits)
##
## l_train_loss = []
## start = time.time()
##
## for batch_idx in arr_splits:
##
## X_batch, Y_batch = X_train[batch_idx], Y_train[batch_idx]
## train_logloss, train_acc = model.train_on_batch(X_batch, Y_batch)
##
## l_train_loss.append([train_logloss, train_acc])
##
## test_logloss, test_acc = model.evaluate(X_test,
## Y_test,
## verbose=0,
## batch_size=64)
## list_train_loss.append(np.mean(np.array(l_train_loss), 0).tolist())
## list_test_loss.append([test_logloss, test_acc])
## list_learning_rate.append(float(K.get_value(model.optimizer.lr)))
## # to convert numpy array to json serializable
## print('Epoch %s/%s, Time: %s' % (e + 1, nb_epoch, time.time() - start))
##
## d_log = {}
## d_log["batch_size"] = batch_size
## d_log["nb_epoch"] = nb_epoch
## d_log["optimizer"] = opt.get_config()
## d_log["train_loss"] = list_train_loss
## d_log["test_loss"] = list_test_loss
## d_log["learning_rate"] = list_learning_rate
##
## json_file = os.path.join('./log/experiment_log_image.json')
## with open(json_file, 'w') as fp:
## json.dump(d_log, fp, indent=4, sort_keys=True)
return model
def conv_block(x, stage, branch, nb_filter, dropout_rate=None, weight_decay=1e-4):
'''Apply BatchNorm, Relu, bottleneck 1x1 Conv2D, 3x3 Conv2D, and option dropout
# Arguments
x: input tensor
stage: index for dense block
branch: layer index within each dense block
nb_filter: number of filters
dropout_rate: dropout rate
weight_decay: weight decay factor
'''
eps = 1.1e-5
conv_name_base = 'conv' + str(stage) + '_' + str(branch)
relu_name_base = 'tanh' + str(stage) + '_' + str(branch)
# 1x1 Convolution (Bottleneck layer)
inter_channel = nb_filter * 4
x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_x1_bn')(x)
x = Scale(axis=concat_axis, name=conv_name_base+'_x1_scale')(x)
x = Activation('tanh', name=relu_name_base+'_x1')(x)
x = Convolution2D(inter_channel, 1, 1, name=conv_name_base+'_x1', bias=False)(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
# 3x3 Convolution
x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_x2_bn')(x)
x = Scale(axis=concat_axis, name=conv_name_base+'_x2_scale')(x)
x = Activation('tanh', name=relu_name_base+'_x2')(x)
x = ZeroPadding2D((1, 1), name=conv_name_base+'_x2_zeropadding')(x)
x = Convolution2D(nb_filter, 3, 3, name=conv_name_base+'_x2', bias=False)(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
return x
def transition_block(x, stage, nb_filter, compression=1.0, dropout_rate=None, weight_decay=1E-4):
''' Apply BatchNorm, 1x1 Convolution, averagePooling, optional compression, dropout
# Arguments
x: input tensor
stage: index for dense block
nb_filter: number of filters
compression: calculated as 1 - reduction. Reduces the number of feature maps in the transition block.
dropout_rate: dropout rate
weight_decay: weight decay factor
'''
eps = 1.1e-5
conv_name_base = 'conv' + str(stage) + '_blk'
relu_name_base = 'tanh' + str(stage) + '_blk'
pool_name_base = 'pool' + str(stage)
x = BatchNormalization(epsilon=eps, axis=concat_axis, name=conv_name_base+'_bn')(x)
x = Scale(axis=concat_axis, name=conv_name_base+'_scale')(x)
x = Activation('tanh', name=relu_name_base)(x)
x = Convolution2D(int(nb_filter * compression), 1, 1, name=conv_name_base, bias=False)(x)
if dropout_rate:
x = Dropout(dropout_rate)(x)
## x = AveragePooling2D((2, 2), strides=(2, 2), name=pool_name_base)(x)
x = AveragePooling2D((1, 1), strides=(1, 1), name=pool_name_base)(x)
return x
def dense_block(x, stage, nb_layers, nb_filter, growth_rate, dropout_rate=None, weight_decay=1e-4, grow_nb_filters=True):
''' Build a dense_block where the output of each conv_block is fed to subsequent ones
# Arguments
x: input tensor
stage: index for dense block
nb_layers: the number of layers of conv_block to append to the model.
nb_filter: number of filters
growth_rate: growth rate
dropout_rate: dropout rate
weight_decay: weight decay factor
grow_nb_filters: flag to decide to allow number of filters to grow
'''
eps = 1.1e-5
concat_feat = x
for i in range(nb_layers):
branch = i+1
x = conv_block(concat_feat, stage, branch, growth_rate, dropout_rate, weight_decay)
concat_feat = merge([concat_feat, x], concat_axis=concat_axis,mode='concat',name='concat_'+str(stage)+'_'+str(branch))
if grow_nb_filters:
nb_filter += growth_rate
return concat_feat, nb_filter
if __name__ == '__main__':
# Example to fine-tune on 3000 samples from Cifar10
img_rows, img_cols = 28, 28 # Resolution of inputs
channel = 4
num_classes = 3
batch_size = 16
nb_epoch = 3
time_start = time.time();
# Load Cifar10 data. Please implement your own load_data() module for your own dataset
## X_train, Y_train, X_valid, Y_valid = load_cifar10_data(img_rows, img_cols)
f1 = open('/home/xm/桌面/论文精华/total_data.txt','rb')
X_train,Y_train,X_valid,Y_valid = pickle.load(f1)
#X_train,Y_train,X_valid,Y_valid = np.load('/home/xm/桌面/论文数据修改/dataset11000.npy')
Y_train = np_utils.to_categorical(Y_train, num_classes)
Y_valid = np_utils.to_categorical(Y_valid, num_classes)
# Load our model
model = densenet121_model(img_rows=img_rows, img_cols=img_cols, color_type=channel, num_classes=num_classes)将数据导入模型
model.fit(X_train, Y_train, batch_size=batch_size, nb_epoch=50,f shuffle=True,
verbose=1,validation_data=(X_valid, Y_valid),callbacks = [history])
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