def apply(inputs):
filters = input_filters * expand_ratio
if expand_ratio != 1:
x = layers.Conv2D(
filters,
kernel_size=kernel_size,
strides=strides,
kernel_initializer=CONV_KERNEL_INITIALIZER,
data_format="channels_last",
padding="same",
use_bias=False,
name=name + "expand_conv",
)(inputs)
x = layers.BatchNormalization(axis=bn_axis,
momentum=bn_momentum,
name=name + "expand_bn")(x)
x = layers.Activation(activation=activation,
name=name + "expand_activation")(x)
else:
x = inputs
# Squeeze and excite
if 0 < se_ratio <= 1:
filters_se = max(1, int(input_filters * se_ratio))
se = layers.GlobalAveragePooling2D(name=name + "se_squeeze")(x)
if bn_axis == 1:
se_shape = (filters, 1, 1)
else:
se_shape = (1, 1, filters)
se = layers.Reshape(se_shape, name=name + "se_reshape")(se)
se = layers.Conv2D(
filters_se,
1,
padding="same",
activation=activation,
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=name + "se_reduce",
)(se)
se = layers.Conv2D(
filters,
1,
padding="same",
activation="sigmoid",
kernel_initializer=CONV_KERNEL_INITIALIZER,
name=name + "se_expand",
)(se)
x = layers.multiply([x, se], name=name + "se_excite")
# Output phase:
x = layers.Conv2D(
output_filters,
kernel_size=1 if expand_ratio != 1 else kernel_size,
strides=1 if expand_ratio != 1 else strides,
kernel_initializer=CONV_KERNEL_INITIALIZER,
padding="same",
use_bias=False,
name=name + "project_conv",
)(x)
x = layers.BatchNormalization(axis=bn_axis,
momentum=bn_momentum,
name=name + "project_bn")(x)
if expand_ratio == 1:
x = layers.Activation(activation=activation,
name=name + "project_activation")(x)
# Residual:
if strides == 1 and input_filters == output_filters:
if survival_probability:
x = layers.Dropout(
survival_probability,
noise_shape=(None, 1, 1, 1),
name=name + "drop",
)(x)
x = layers.add([x, inputs], name=name + "add")
return x
# -*- coding: utf-8 -*-
"""
Created on Sun Sep 24 15:13:56 2017
@author: ihong
ch5 - DL for computer vision
"""
# %% Convenet
from keras import layers
from keras import models
model = models.Sequential()
model.add(layers.Conv2D(32, (3, 3), activation='relu',
input_shape=(28, 28, 1)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
#model.summary()
# Dense connected layers
model.add(layers.Flatten())
model.add(layers.Dense(64, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.summary()
## Triaining convnet -- MNIST
from keras.datasets import mnist
from keras.utils import to_categorical
import keras
from keras import layers
from keras import models
from keras import optimizers
from keras.preprocessing.image import ImageDataGenerator
import matplotlib.pyplot as plt
import tensorflowjs as tfjs
model = models.Sequential()
model.add(
layers.Conv2D(32, (3, 3), activation='relu', input_shape=(100, 100, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation='relu'))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dense(512, activation='relu'))
model.add(layers.Dense(10, activation='softmax'))
model.compile(loss='categorical_crossentropy',
optimizer='adam',
metrics=['acc'])
classes = [
'zero', 'one', 'two', 'three', 'four', 'five', 'seven', 'eight', 'nine'
]
train_dir = './static/img/dataset/train'
import keras
# reznet = keras.applications.resnet.ResNet50(include_top=False, weights=None, input_tensor=None, input_shape=(51, 51, 1), pooling=None, classes=1000)
# out = reznet.get_layer('conv5_block3_add').output
# out = keras.layers.Flatten()(out)
# out = keras.layers.Dense(1024, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001), bias_regularizer=keras.regularizers.l2(0.001))(out)
# out = keras.layers.Dense(128, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001), bias_regularizer=keras.regularizers.l2(0.001))(out)
# out = keras.layers.Dense(32, activation='relu', kernel_regularizer=keras.regularizers.l2(0.001), bias_regularizer=keras.regularizers.l2(0.001))(out)
# out = keras.layers.Dense(9)(out)
# model = keras.Model(reznet.input, out)
#
# import numpy as np
# a = np.zeros((51,51,1))
# model.predict(a.reshape(1,51,51,1))
# print(model.summary())
from keras import layers, regularizers
in1 = layers.Input((51, 51, 1,))
m1 = layers.Conv2D(32, (4, 4), strides=(2, 2), activation='relu', input_shape=(51, 51, 1))(in1)
m1 = layers.Conv2D(64, (4, 4), strides=(2, 2), activation='relu')(m1)
m1 = layers.Conv2D(64, (3, 3), strides=(2, 2), activation='relu')(m1)
m1 = layers.Conv2D(64, (2, 2), strides=(1, 1), activation='relu')(m1)
m1 = layers.Flatten()(m1)
conv_model = keras.Model(in1, m1)
out = layers.Dense(512, activation='relu', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=regularizers.l2(0.001))(m1)
out = layers.Dense(32, activation='relu', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=regularizers.l2(0.001))(out)
out = layers.Dense(16, activation='relu', kernel_regularizer=regularizers.l2(0.001), bias_regularizer=regularizers.l2(0.001))(out)
out = layers.Dense(9)(out)
model = keras.Model(in1, out)
model.summary()
def Compute_parameters(n,m,k,l):
"""
How to compute the number of parameters in each cnn layer:
Definition n--width of filter
m--height of filter
k--number of input feature maps
l--number of output feature maps
Then number of paramters #= (n*m*k+1)*l
"""
print "# of paramteres in this layer=",(n*m*k+1)*l
if __name__=='__main__':
model=models.Sequential()
model.add(layers.Conv2D(32,(3,3),activation='relu',input_shape=(28,28,1))) ##32 is the depth of the kernal,(3,3) is the filter/kernal shape
Compute_parameters(3,3,1,32)
model.add(layers.MaxPooling2D(2,2))
model.add(layers.Conv2D(64,(3,3),activation='relu'))
Compute_parameters(3,3,32,64)
model.add(layers.MaxPooling2D(2,2))
model.add(layers.Conv2D(64,(3,3),activation='relu'))
Compute_parameters(3,3,64,64)
model.add(layers.Flatten())
model.add(layers.Dense(64,activation='relu'))
model.add(layers.Dense(10,activation='softmax'))
print model.summary()
(train_images,train_labels),(test_images,test_labels) = mnist.load_data()
train_images=train_images.reshape((60000,28,28,1))
train_images=train_images.astype('float32')/255
test_images=test_images.reshape((10000,28,28,1))
# model.add(layers.Dropout(0.5))
# model.add(layers.Dense(512, activation='relu'))
# model.add(layers.Dense(5, activation='softmax'))
# Conv2D since learning about images
# Maxpooling to halve the input in both dim
# AvgPool - Sum all of the values and dividing it by the total number of values
# MaxPool - Selecting the maximum value
# Dropout is used to prevent overfitting
# last layer is softmax since it is good for multi-class - gives prob
#
model = models.Sequential()
model.add(
layers.Conv2D(filters=20,
kernel_size=(3, 3),
activation='relu',
input_shape=(img_size, img_size, 3)))
# model.add(layers.Dropout(0.25))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(filters=40, kernel_size=(3, 3), activation='relu'))
model.add(layers.Dropout(0.25))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(filters=80, kernel_size=(3, 3), activation='relu'))
# model.add(layers.Dropout(0.25))
# model.add(layers.Conv2D(filters=80, kernel_size=(3,3), activation='relu'))
model.add(layers.Dropout(0.25))
model.add(layers.MaxPooling2D(pool_size=(2, 2)))
model.add(layers.Conv2D(filters=160, kernel_size=(3, 3), activation='relu'))
batch_size=20,
class_mode='binary')
validation_generator = validation_data_gen.flow_from_directory(
validation_dir, target_size=(150, 150), batch_size=20, class_mode='binary')
# ImageDataGenerator()函数参数说明
# rotation_range：是角度值（0~180），表示图像随机旋转的角度范围
# width_shift, height_shift：图像在水平或者垂直方向上平移的范围（相对于总宽度或者总高度的比例）
# shear_range：随机错切变换的角度
# zoom_range：随机缩放的范围
# horizontal_flip：随机将一半图像水平翻转（因为现实世界的图像很少水平对称）
# fill_mode：用于填充新创建像素的方法，创建这些新的像素用于填充旋转或者平移产生的像素缺失
model = models.Sequential()
model.add(
layers.Conv2D(32, (3, 3),
activation=activations.relu,
input_shape=(150, 150, 3)))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(64, (3, 3), activation=activations.relu))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation=activations.relu))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Conv2D(128, (3, 3), activation=activations.relu))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.Flatten())
model.add(layers.Dropout(0.5))
model.add(layers.Dense(512, activation=activations.relu))
model.add(layers.Dense(1, activation=activations.sigmoid))
model.compile(loss=losses.binary_crossentropy,
optimizer=optimizers.rmsprop(lr=1e-4),
test_images = test_images.reshape(10000, 28, 28, 1)
input_shape = (28, 28, 1)
train_images = train_images.astype('float32') / 255.0
test_images = test_images.astype('float32') / 255.0
# ラベルの準備
train_labels = to_categorical(train_labels, 10)
print("train_labels[0] : ", train_labels[0])
test_labels = to_categorical(test_labels, 10)
print("test_labels[0] : ", test_labels[0])
# ニューラルネットワークの構築とコンパイル
network = models.Sequential()
network.add(
layers.Conv2D(32,
kernel_size=(3, 3),
activation='relu',
input_shape=input_shape))
network.add(layers.Conv2D(64, (3, 3), activation='relu'))
network.add(layers.MaxPooling2D(pool_size=(2, 2)))
network.add(layers.Dropout(0.25))
network.add(layers.Flatten())
network.add(layers.Dense(128, activation='relu'))
network.add(layers.Dropout(0.5))
network.add(layers.Dense(10, activation='softmax'))
network.compile(optimizer='adadelta',
loss='categorical_crossentropy',
metrics=['accuracy'])
network.summary()
# 学習
network.fit(train_images, train_labels, epochs=20, batch_size=128)
images.append(img)
elif file_name == label_file_name:
for l in open('{}\\{}'.format(data_dir, file_name), 'r'):
lbl = [0] * 3
lbl[int(l)] = 1
labels.append(lbl)
# plt.imshow(images[0])
# plt.show()
# print(labels)
# print(images[0])
# 建立CNN模型：卷积层-池化层-卷积层-全连接层
model = models.Sequential()
model.add(
layers.Conv2D(16, (3, 3),
padding='same',
activation='relu',
input_shape=(INPUT_SIZE, INPUT_SIZE, 1)))
model.add(layers.MaxPooling2D((4, 4)))
model.add(layers.Conv2D(16, (3, 3), padding='same', activation='relu'))
model.add(layers.Flatten())
model.add(layers.Dense(32, activation='relu'))
model.add(layers.Dense(3, activation='softmax'))
model.summary()
model.compile(optimizer='adam',
loss='categorical_crossentropy',
metrics=['accuracy'])
# 训练模型
train_len = int(len(images) * TRAIN_PERCENTAGE)
train_images = np.array(images[:train_len]).reshape(
def conv_block(input_tensor,
kernel_size,
filters,
stage,
block,
strides=(2, 2)):
"""A block that has a conv layer at shortcut.
# Arguments
input_tensor: input tensor
kernel_size: default 3, the kernel size of
middle conv layer at main path
filters: list of integers, the filters of 3 conv layer at main path
stage: integer, current stage label, used for generating layer names
block: 'a','b'..., current block label, used for generating layer names
strides: Strides for the first conv layer in the block.
# Returns
Output tensor for the block.
Note that from stage 3,
the first conv layer at main path is with strides=(2, 2)
And the shortcut should have strides=(2, 2) as well
"""
filters1, filters2, filters3 = filters
if backend.image_data_format() == 'channels_last':
bn_axis = 3
else:
bn_axis = 1
conv_name_base = 'res' + str(stage) + block + '_branch'
bn_name_base = 'bn' + str(stage) + block + '_branch'
x = layers.Conv2D(filters1, (1, 1),
strides=strides,
kernel_initializer='he_normal',
name=conv_name_base + '2a')(input_tensor)
x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2a')(x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(filters2,
kernel_size,
padding='same',
kernel_initializer='he_normal',
name=conv_name_base + '2b')(x)
x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2b')(x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(filters3, (1, 1),
kernel_initializer='he_normal',
name=conv_name_base + '2c')(x)
x = layers.BatchNormalization(axis=bn_axis, name=bn_name_base + '2c')(x)
shortcut = layers.Conv2D(filters3, (1, 1),
strides=strides,
kernel_initializer='he_normal',
name=conv_name_base + '1')(input_tensor)
shortcut = layers.BatchNormalization(axis=bn_axis,
name=bn_name_base + '1')(shortcut)
x = layers.add([x, shortcut])
x = layers.Activation('relu')(x)
return x
# Scale image data from range 0:255 to range 0:1
X_train = X_train / 255.0
X_test = X_test / 255.0
# one-hot encode the labels
Y_train = utils.to_categorical(Y_train)
Y_test = utils.to_categorical(Y_test)
##############################################
# Simple custom CNN Keras functional model
##############################################
inputs = layers.Input(shape=(32, 32, 3))
net = layers.Conv2D(32, kernel_size=(3, 3), padding='same')(inputs)
net = layers.Activation('relu')(net)
net = layers.BatchNormalization()(net)
net = layers.MaxPooling2D(pool_size=(2,2))(net)
net = layers.Conv2D(64, kernel_size=(3, 3), padding='same')(net)
net = layers.Activation('relu')(net)
net = layers.BatchNormalization()(net)
net = layers.MaxPooling2D(pool_size=(2,2))(net)
net = layers.Flatten()(net)
net = layers.Dropout(0.4)(net)
net = layers.Dense(512)(net)
net = layers.Activation('relu')(net)
net = layers.Dropout(0.4)(net)
data_format='channels_first'),
], ids=['GRU', 'LSTM', 'ConvLSTM2D'])
def test_preprocess_weights_for_loading(layer):
# A model is needed to initialize weights.
_ = Sequential([layer])
weights1 = layer.get_weights()
weights2 = saving.preprocess_weights_for_loading(
layer, convert_weights(layer, weights1),
original_keras_version='1')
assert all([np.allclose(x, y, 1e-5)
for (x, y) in zip(weights1, weights2)])
@keras_test
@pytest.mark.parametrize("layer", [
layers.Conv2D(2, (3, 3), input_shape=[5, 5, 3]),
layers.Conv2DTranspose(2, (5, 5),
input_shape=[7, 7, 3],
data_format='channels_first'),
], ids=['Conv2D', 'Conv2DTranspose'])
def test_preprocess_weights_for_loading_for_model(layer):
model = Sequential([layer])
weights1 = model.get_weights()
weights2 = saving.preprocess_weights_for_loading(
model, convert_weights(layer, weights1),
original_keras_version='1')
assert all([np.allclose(x, y, 1e-5)
for (x, y) in zip(weights1, weights2)])
@keras_test
from keras.utils import to_categorical
from keras.preprocessing.image import ImageDataGenerator
data = pd.read_csv('./train.csv')
test = pd.read_csv('./test.csv')
labels = to_categorical(data.iloc[:, 0].values)
inputs = data.iloc[:, 1:].values.reshape(-1, 28, 28, 1)
x_train, x_test, y_train, y_test = train_test_split(inputs, labels)
model = models.Sequential()
model.add(
layers.Conv2D(128, (7, 7),
activation='relu',
input_shape=(28, 28, 1),
padding='same'))
model.add(layers.Dropout(0.25))
model.add(layers.BatchNormalization())
model.add(layers.Conv2D(128, (7, 7), activation='relu', padding='same'))
model.add(layers.Dropout(0.25))
model.add(layers.MaxPooling2D((2, 2)))
model.add(layers.BatchNormalization())
model.add(layers.Conv2D(128, (5, 5), activation='relu', padding='same'))
model.add(layers.Dropout(0.25))
model.add(layers.BatchNormalization())
model.add(layers.Conv2D(128, (5, 5), activation='relu', padding='same'))
model.add(layers.Dropout(0.25))
def EfficientNetV2(
width_coefficient,
depth_coefficient,
default_size,
dropout_rate=0.2,
drop_connect_rate=0.2,
depth_divisor=8,
min_depth=8,
bn_momentum=0.9,
activation="swish",
blocks_args="default",
model_name="efficientnetv2",
include_top=True,
weights="imagenet",
input_tensor=None,
input_shape=None,
pooling=None,
classes=1000,
classifier_activation="softmax",
include_preprocessing=True,
):
"""Instantiates the EfficientNetV2 architecture using given scaling coefficients.
Args:
width_coefficient: float, scaling coefficient for network width.
depth_coefficient: float, scaling coefficient for network depth.
default_size: integer, default input image size.
dropout_rate: float, dropout rate before final classifier layer.
drop_connect_rate: float, dropout rate at skip connections.
depth_divisor: integer, a unit of network width.
min_depth: integer, minimum number of filters.
bn_momentum: float. Momentum parameter for Batch Normalization layers.
activation: activation function.
blocks_args: list of dicts, parameters to construct block modules.
model_name: string, model name.
include_top: whether to include the fully-connected layer at the top of the
network.
weights: one of `None` (random initialization), `"imagenet"` (pre-training
on ImageNet), or the path to the weights file to be loaded.
input_tensor: optional Keras tensor (i.e. output of `layers.Input()`) or
numpy array to use as image input for the model.
input_shape: optional shape tuple, only to be specified if `include_top` is
False. It should have exactly 3 inputs channels.
pooling: optional pooling mode for feature extraction when `include_top` is
`False`. - `None` means that the output of the model will be the 4D tensor
output of the last convolutional layer. - "avg" means that global average
pooling will be applied to the output of the last convolutional layer, and
thus the output of the model will be a 2D tensor. - `"max"` means that
global max pooling will be applied.
classes: optional number of classes to classify images into, only to be
specified if `include_top` is True, and if no `weights` argument is
specified.
classifier_activation: A string or callable. The activation function to use
on the `"top"` layer. Ignored unless `include_top=True`. Set
`classifier_activation=None` to return the logits of the `"top"` layer.
include_preprocessing: Boolean, whether to include the preprocessing layer
(`Rescaling`) at the bottom of the network. Defaults to `True`.
Returns:
A `keras.Model` instance.
Raises:
ValueError: in case of invalid argument for `weights`,
or invalid input shape.
ValueError: if `classifier_activation` is not `"softmax"` or `None` when
using a pretrained top layer.
"""
if blocks_args == "default":
blocks_args = DEFAULT_BLOCKS_ARGS[model_name]
if not (weights in {"imagenet", None} or tf.io.gfile.exists(weights)):
raise ValueError("The `weights` argument should be either "
"`None` (random initialization), `imagenet` "
"(pre-training on ImageNet), "
"or the path to the weights file to be loaded."
f"Received: weights={weights}")
if weights == "imagenet" and include_top and classes != 1000:
raise ValueError(
"If using `weights` as `'imagenet'` with `include_top`"
" as true, `classes` should be 1000"
f"Received: classes={classes}")
# Determine proper input shape
input_shape = imagenet_utils.obtain_input_shape(
input_shape,
default_size=default_size,
min_size=32,
data_format=backend.image_data_format(),
require_flatten=include_top,
weights=weights)
if input_tensor is None:
img_input = layers.Input(shape=input_shape)
else:
if not backend.is_keras_tensor(input_tensor):
img_input = layers.Input(tensor=input_tensor, shape=input_shape)
else:
img_input = input_tensor
bn_axis = 3 if backend.image_data_format() == "channels_last" else 1
x = img_input
if include_preprocessing:
# Apply original V1 preprocessing for Bx variants
# if number of channels allows it
num_channels = input_shape[bn_axis - 1]
if model_name.split("-")[-1].startswith("b") and num_channels == 3:
x = layers.Rescaling(scale=1. / 255)(x)
x = layers.Normalization(
mean=[0.485, 0.456, 0.406],
variance=[0.229**2, 0.224**2, 0.225**2],
axis=bn_axis,
)(x)
else:
x = layers.Rescaling(scale=1. / 128.0, offset=-1)(x)
# Build stem
stem_filters = round_filters(
filters=blocks_args[0]["input_filters"],
width_coefficient=width_coefficient,
min_depth=min_depth,
depth_divisor=depth_divisor,
)
x = layers.Conv2D(
filters=stem_filters,
kernel_size=3,
strides=2,
kernel_initializer=CONV_KERNEL_INITIALIZER,
padding="same",
use_bias=False,
name="stem_conv",
)(x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=bn_momentum,
name="stem_bn",
)(x)
x = layers.Activation(activation, name="stem_activation")(x)
# Build blocks
blocks_args = copy.deepcopy(blocks_args)
b = 0
blocks = float(sum(args["num_repeat"] for args in blocks_args))
for (i, args) in enumerate(blocks_args):
assert args["num_repeat"] > 0
# Update block input and output filters based on depth multiplier.
args["input_filters"] = round_filters(
filters=args["input_filters"],
width_coefficient=width_coefficient,
min_depth=min_depth,
depth_divisor=depth_divisor)
args["output_filters"] = round_filters(
filters=args["output_filters"],
width_coefficient=width_coefficient,
min_depth=min_depth,
depth_divisor=depth_divisor)
# Determine which conv type to use:
block = {0: MBConvBlock, 1: FusedMBConvBlock}[args.pop("conv_type")]
repeats = round_repeats(repeats=args.pop("num_repeat"),
depth_coefficient=depth_coefficient)
for j in range(repeats):
# The first block needs to take care of stride and filter size increase.
if j > 0:
args["strides"] = 1
args["input_filters"] = args["output_filters"]
x = block(
activation=activation,
bn_momentum=bn_momentum,
survival_probability=drop_connect_rate * b / blocks,
name="block{}{}_".format(i + 1, chr(j + 97)),
**args,
)(x)
# Build top
top_filters = round_filters(filters=1280,
width_coefficient=width_coefficient,
min_depth=min_depth,
depth_divisor=depth_divisor)
x = layers.Conv2D(
filters=top_filters,
kernel_size=1,
strides=1,
kernel_initializer=CONV_KERNEL_INITIALIZER,
padding="same",
data_format="channels_last",
use_bias=False,
name="top_conv",
)(x)
x = layers.BatchNormalization(
axis=bn_axis,
momentum=bn_momentum,
name="top_bn",
)(x)
x = layers.Activation(activation=activation, name="top_activation")(x)
if include_top:
x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
if dropout_rate > 0:
x = layers.Dropout(dropout_rate, name="top_dropout")(x)
imagenet_utils.validate_activation(classifier_activation, weights)
x = layers.Dense(classes,
activation=classifier_activation,
kernel_initializer=DENSE_KERNEL_INITIALIZER,
bias_initializer=tf.constant_initializer(0),
name="predictions")(x)
else:
if pooling == "avg":
x = layers.GlobalAveragePooling2D(name="avg_pool")(x)
elif pooling == "max":
x = layers.GlobalMaxPooling2D(name="max_pool")(x)
# Ensure that the model takes into account
# any potential predecessors of `input_tensor`.
if input_tensor is not None:
inputs = layer_utils.get_source_inputs(input_tensor)
else:
inputs = img_input
# Create model.
model = training.Model(inputs, x, name=model_name)
# Load weights.
if weights == "imagenet":
if include_top:
file_suffix = ".h5"
file_hash = WEIGHTS_HASHES[model_name[-2:]][0]
else:
file_suffix = "_notop.h5"
file_hash = WEIGHTS_HASHES[model_name[-2:]][1]
file_name = model_name + file_suffix
weights_path = data_utils.get_file(file_name,
BASE_WEIGHTS_PATH + file_name,
cache_subdir="models",
file_hash=file_hash)
model.load_weights(weights_path)
elif weights is not None:
model.load_weights(weights)
return model
print('# of validation images:', validation['features'].shape[0])
# Pad images with 0s
train['features'] = np.pad(train['features'], ((0, 0), (2, 2), (2, 2), (0, 0)),
'constant')
validation['features'] = np.pad(validation['features'],
((0, 0), (2, 2), (2, 2), (0, 0)), 'constant')
test['features'] = np.pad(test['features'], ((0, 0), (2, 2), (2, 2), (0, 0)),
'constant')
print("Updated Image Shape: {}".format(train['features'][0].shape))
model = keras.Sequential()
model.add(
layers.Conv2D(filters=6,
kernel_size=(3, 3),
activation='relu',
input_shape=(32, 32, 1)))
model.add(layers.AveragePooling2D())
model.add(layers.Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))
model.add(layers.AveragePooling2D())
model.add(layers.Flatten())
model.add(layers.Dense(units=120, activation='relu'))
model.add(layers.Dense(units=84, activation='relu'))
model.add(layers.Dense(units=10, activation='softmax'))
model.compile(loss=keras.losses.categorical_crossentropy,
optimizer=keras.optimizers.Adam(),
metrics=['accuracy'])
X_train, y_train = train['features'], to_categorical(train['labels'])
X_validation, y_validation = validation['features'], to_categorical(
validation['labels'])
def construct_graph(self, input_tensor, stage5=True):
assert self.input_tensor is not None, "input_tensor can not be none!"
# Stage 1
x = KL.ZeroPadding2D((3, 3))(input_tensor)
x = KL.Conv2D(64, (7, 7), strides=(2, 2), name='conv1',
use_bias=True)(x)
x = BatchNorm(axis=3, name='bn_conv1')(x)
x = KL.Activation('relu')(x)
C1 = x = KL.MaxPooling2D((3, 3), strides=(2, 2), padding="same")(x)
# Stage 2
x = self.conv_block(x,
3, [64, 64, 256],
stage=2,
block='a',
strides=(1, 1))
x = self.identity_block(x, 3, [64, 64, 256], stage=2, block='b')
C2 = x = self.identity_block(x, 3, [64, 64, 256], stage=2, block='c')
# Stage 3
x = self.conv_block(x, 3, [128, 128, 512], stage=3, block='a')
x = self.identity_block(x, 3, [128, 128, 512], stage=3, block='b')
x = self.identity_block(x, 3, [128, 128, 512], stage=3, block='c')
C3 = x = self.identity_block(x, 3, [128, 128, 512], stage=3, block='d')
# Stage 4
x = self.conv_block(x, 3, [256, 256, 1024], stage=4, block='a')
block_count = {"resnet50": 5, "resnet101": 22}[self.architecture]
for i in range(block_count):
x = self.identity_block(x,
3, [256, 256, 1024],
stage=4,
block=chr(98 + i))
C4 = x
# Stage 5
if stage5:
x = self.conv_block(x, 3, [512, 512, 2048], stage=5, block='a')
x = self.identity_block(x, 3, [512, 512, 2048], stage=5, block='b')
C5 = x = self.identity_block(x,
3, [512, 512, 2048],
stage=5,
block='c')
else:
C5 = None
P5 = KL.Conv2D(256, (1, 1), name='fpn_c5p5')(C5)
P4 = KL.Add(name="fpn_p4add")([
KL.UpSampling2D(size=(2, 2), name="fpn_p5upsampled")(P5),
KL.Conv2D(256, (1, 1), name='fpn_c4p4')(C4)
])
P3 = KL.Add(name="fpn_p3add")([
KL.UpSampling2D(size=(2, 2), name="fpn_p4upsampled")(P4),
KL.Conv2D(256, (1, 1), name='fpn_c3p3')(C3)
])
P2 = KL.Add(name="fpn_p2add")([
KL.UpSampling2D(size=(2, 2), name="fpn_p3upsampled")(P3),
KL.Conv2D(256, (1, 1), name='fpn_c2p2')(C2)
])
# Attach 3x3 conv to all P layers to get the final feature maps.
P2 = KL.Conv2D(256, (3, 3), padding="SAME", name="fpn_p2")(P2)
P3 = KL.Conv2D(256, (3, 3), padding="SAME", name="fpn_p3")(P3)
P4 = KL.Conv2D(256, (3, 3), padding="SAME", name="fpn_p4")(P4)
P5 = KL.Conv2D(256, (3, 3), padding="SAME", name="fpn_p5")(P5)
# P6 is used for the 5th anchor scale in RPN. Generated by
# subsampling from P5 with stride of 2.
P6 = KL.MaxPooling2D(pool_size=(1, 1), strides=2, name="fpn_p6")(P5)
self.output_layers = [P2, P3, P4, P5, P6]
def vgg16(input_shape=(512, 512, 3),
input_tensor=None,
pretrained_weights_path=None,
output_stride=16):
if input_tensor is None:
img_input = layers.Input(shape=input_shape, name='img_input')
else:
img_input = input_tensor
x = layers.BatchNormalization()(img_input)
# Block 1
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1')(x)
x = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
f1 = x
# Block 2
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1')(x)
x = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
f2 = x
# Block 3
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2')(x)
x = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3')(x)
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
f3 = x
# Block 4
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3')(x)
if output_stride > 16:
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x)
f4 = x
# Block 5
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2')(x)
x = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3')(x)
if output_stride > 8:
x = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x)
f5 = x
if pretrained_weights_path is not None and os.path.exists(
pretrained_weights_path):
log('load pretrained encoder weights from `{}`'.format(
pretrained_weights_path))
Model(img_input, x,
name='vgg16_encoder').load_weights(pretrained_weights_path)
if output_stride == 8:
features = [f1, f2, f5, f5, f5]
elif output_stride == 16:
features = [f1, f2, f3, f5, f5]
else:
features = [f1, f2, f3, f4, f5]
return img_input, features
def _depthwise_conv_block(inputs, pointwise_conv_filters, alpha,
depth_multiplier=1, strides=(1, 1), block_id=1):
"""Adds a depthwise convolution block.
A depthwise convolution block consists of a depthwise conv,
batch normalization, relu6, pointwise convolution,
batch normalization and relu6 activation.
# Arguments
inputs: Input tensor of shape `(rows, cols, channels)`
(with `channels_last` data format) or
(channels, rows, cols) (with `channels_first` data format).
pointwise_conv_filters: Integer, the dimensionality of the output space
(i.e. the number of output filters in the pointwise convolution).
alpha: controls the width of the network.
- If `alpha` < 1.0, proportionally decreases the number
of filters in each layer.
- If `alpha` > 1.0, proportionally increases the number
of filters in each layer.
- If `alpha` = 1, default number of filters from the paper
are used at each layer.
depth_multiplier: The number of depthwise convolution output channels
for each input channel.
The total number of depthwise convolution output
channels will be equal to `filters_in * depth_multiplier`.
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.
Specifying any stride value != 1 is incompatible with specifying
any `dilation_rate` value != 1.
block_id: Integer, a unique identification designating
the block number.
# Input shape
4D tensor with shape:
`(batch, channels, rows, cols)` if data_format='channels_first'
or 4D tensor with shape:
`(batch, rows, cols, channels)` if data_format='channels_last'.
# Output shape
4D tensor with shape:
`(batch, filters, new_rows, new_cols)`
if data_format='channels_first'
or 4D tensor with shape:
`(batch, new_rows, new_cols, filters)`
if data_format='channels_last'.
`rows` and `cols` values might have changed due to stride.
# Returns
Output tensor of block.
"""
channel_axis = 1 if backend.image_data_format() == 'channels_first' else -1
pointwise_conv_filters = int(pointwise_conv_filters * alpha)
if strides == (1, 1):
x = inputs
else:
x = layers.ZeroPadding2D(((0, 1), (0, 1)),
name='conv_pad_%d' % block_id)(inputs)
x = layers.DepthwiseConv2D((3, 3),
padding='same' if strides == (1, 1) else 'valid',
depth_multiplier=depth_multiplier,
strides=strides,
use_bias=False,
name='conv_dw_%d' % block_id)(x)
x = layers.BatchNormalization(
axis=channel_axis, name='conv_dw_%d_bn' % block_id)(x)
x = layers.ReLU(6., name='conv_dw_%d_relu' % block_id)(x)
x = layers.Conv2D(pointwise_conv_filters, (1, 1),
padding='same',
use_bias=False,
strides=(1, 1),
name='conv_pw_%d' % block_id)(x)
x = layers.BatchNormalization(axis=channel_axis,
name='conv_pw_%d_bn' % block_id)(x)
return layers.ReLU(6., name='conv_pw_%d_relu' % block_id)(x)
is_sobel=args.sobel,
channel=args.channel,
is_gray=args.is_gray,
orig=True)
#build
input_x = kl.Input(shape=args.input_sz, name='input1')
input_gx = kl.Input(shape=args.input_sz, name='input2')
base_model = BaseModel(args.input_sz)
head_a = [
kl.Conv2D(
args.cluster_a,
kernel_size=1,
strides=1,
activation='softmax',
kernel_initializer=ki.he_normal(),
# bias_initializer = ki.Zeros(),
name='a' + str(i)) for i in range(args.head_a_num)
]
head_b = kl.Conv2D(
args.cluster_b,
kernel_size=1,
strides=1,
activation='softmax',
kernel_initializer=ki.he_normal(),
# bias_initializer = ki.Zeros(),
name='b')
x = base_model(input_x)
def get_fcn8s_model(input_shape=(224, 224, 3), class_no=21):
"""
FCN 8 模型
:param input_shape: （输入图片长，输入图片宽，RGB层数）,注意长宽最好是32的倍数
:param class_no: 类别数量
:return: Keras模型
"""
input_tensor = layers.Input(shape=input_shape)
x = layers.ZeroPadding2D(padding=(99, 99))(input_tensor) # Pad 100, 99 + 1 in first layer of vgg
with tf.variable_scope("vgg_encoder"):
encoder = VGG16(input_tensor=x, include_top=False, weights='imagenet')
with tf.variable_scope("vgg_decoder"):
with tf.variable_scope("fcn_32s"):
x = encoder.get_layer('block5_pool').output # 拿pool5的输出
# 卷积做降采用
x = layers.Conv2D(filters=4096, kernel_size=(7, 7), activation='relu', padding='valid', name='fc6')(x)
x = layers.Dropout(0.5)(x)
x = layers.Conv2D(filters=4096, kernel_size=(1, 1), activation='relu', padding='valid', name='fc7')(x)
x = layers.Dropout(0.5)(x)
# 使用 1x1卷积 做卷积操作，模拟全链接层操作
x = layers.Conv2D(filters=class_no, kernel_size=(1, 1), padding='valid')(x)
# 使用反卷积做Upsampling到2倍
x = layers.Conv2DTranspose(filters=class_no, kernel_size=(4, 4), strides=(2, 2), padding='same',
use_bias=False, name='upsampling1')(x)
with tf.variable_scope("fcn_16s"):
pool4_output = encoder.get_layer('block4_pool').output # 拿pool4的输出
pool4_output = layers.Conv2D(filters=class_no, kernel_size=(1, 1), padding='valid')(pool4_output)
# 裁剪到2x2 大小
pool4_crop = FCN.center_crop(pool4_output, x)
x = layers.add([x, pool4_crop])
with tf.variable_scope("fcn_8s"):
pool3_output = encoder.get_layer('block3_pool').output # 拿pool3的输出
pool3_output = layers.Conv2D(filters=class_no, kernel_size=(1, 1), padding='valid')(pool3_output)
# 使用反卷积做Upsampling到4倍
x = layers.Conv2DTranspose(filters=class_no, kernel_size=(4, 4), strides=(2, 2),
padding='same', use_bias=False, name='upsampling2')(x)
# 中心裁剪
pool3_crop = FCN.center_crop(pool3_output, x)
x = layers.add([x, pool3_crop])
# 使用反卷积做Upsampling
x = layers.Conv2DTranspose(filters=class_no, kernel_size=(16, 16), strides=(8, 8), padding='same',
use_bias=False, name='upsampling3')(x)
# 如果size不够，再做一个Bilinear的Upsampling（通常在图片size不为32的倍数时候需要）
if K.int_shape(x)[1:3] != K.int_shape(input_tensor)[1:3]:
print('Size different, do Bilinear Upsampling')
x = layers.Lambda(lambda x: tf.image.resize_bilinear(x, size=K.int_shape(input_tensor)[1:3]))(x)
# 对输出的每一个像素的各类别（即各通道）的输出使用softmax
x = layers.Activation('softmax', name='output')(x)
model = models.Model(inputs=input_tensor, outputs=x)
return model
y_val.shape
# In[37]:
#add in a Conv2D (64) and maxPooling2D ((2, 2))
#now, use dropout (0.05, 0.15, 0.25, 0.50)
#change dense 256 -> 512 ...
#only use low batch_size=64
model30 = models.Sequential()
model30.add(layers.Conv2D(64, (3, 3), activation='relu',
input_shape=(64, 64, 1)))
model30.add(layers.MaxPooling2D((2, 2)))
model30.add(layers.Flatten())
model30.add(layers.Dropout(0.05))
model30.add(layers.Dense(256, activation='relu'))
model30.add(layers.Dense(1, activation='sigmoid'))
##########################################################
model30.compile(loss='binary_crossentropy',
optimizer=optimizers.RMSprop(lr=1e-4),
metrics=['acc'])
##########################################################
processing_time = []
def EfficientNet(input_shape,
block_args_list,
global_params,
include_top=True,
pooling=None):
batch_norm_momentum = global_params.batch_norm_momentum
batch_norm_epsilon = global_params.batch_norm_epsilon
if global_params.data_format == "channels_first":
channel_axis = 1
else:
channel_axis = -1
# Stem part
inputs = KL.Input(shape=input_shape)
x = inputs
x = KL.Conv2D(
filters=round_filters(32, global_params),
kernel_size=[3, 3],
strides=[2, 2],
kernel_initializer=conv_kernel_initializer,
padding="same",
use_bias=False,
)(x)
x = KL.BatchNormalization(axis=channel_axis,
momentum=batch_norm_momentum,
epsilon=batch_norm_epsilon)(x)
x = Swish()(x)
# Blocks part
block_idx = 1
n_blocks = sum([block_args.num_repeat for block_args in block_args_list])
drop_rate = global_params.drop_connect_rate or 0
drop_rate_dx = drop_rate / n_blocks
for block_args in block_args_list:
assert block_args.num_repeat > 0
# Update block input and output filters based on depth multiplier.
block_args = block_args._replace(
input_filters=round_filters(block_args.input_filters,
global_params),
output_filters=round_filters(block_args.output_filters,
global_params),
num_repeat=round_repeats(block_args.num_repeat, global_params),
)
# The first block needs to take care of stride and filter size increase.
x = MBConvBlock(block_args,
global_params,
drop_connect_rate=drop_rate_dx * block_idx)(x)
block_idx += 1
if block_args.num_repeat > 1:
block_args = block_args._replace(
input_filters=block_args.output_filters, strides=[1, 1])
for _ in xrange(block_args.num_repeat - 1):
x = MBConvBlock(block_args,
global_params,
drop_connect_rate=drop_rate_dx * block_idx)(x)
block_idx += 1
# Head part
x = KL.Conv2D(
filters=round_filters(1280, global_params),
kernel_size=[1, 1],
strides=[1, 1],
kernel_initializer=conv_kernel_initializer,
padding="same",
use_bias=False,
)(x)
x = KL.BatchNormalization(axis=channel_axis,
momentum=batch_norm_momentum,
epsilon=batch_norm_epsilon)(x)
x = Swish()(x)
if include_top:
x = KL.GlobalAveragePooling2D(data_format=global_params.data_format)(x)
if global_params.dropout_rate > 0:
x = KL.Dropout(global_params.dropout_rate)(x)
x = KL.Dense(global_params.num_classes,
kernel_initializer=dense_kernel_initializer)(x)
x = KL.Activation("softmax")(x)
else:
if pooling == "avg":
x = KL.GlobalAveragePooling2D(
data_format=global_params.data_format)(x)
elif pooling == "max":
x = KL.GlobalMaxPooling2D(data_format=global_params.data_format)(x)
outputs = x
model = KM.Model(inputs, outputs)
return model
def get_model():
inpl = layers.Input((360, 640, 3))
inpr = layers.Input((360, 640, 3))
inpc = layers.Input((360, 640, 3))
xl = layers.Conv2D(32, 3, strides=(2, 2))(inpl)
xl = layers.BatchNormalization()(xl)
xl = layers.Activation('relu')(xl)
xl = layers.Conv2D(32, 3)(xl)
xl = layers.BatchNormalization()(xl)
xl = layers.Activation('relu')(xl)
# xl = layers.MaxPooling2D()(xl)
xr = layers.Conv2D(32, 3, strides=(2, 2))(inpr)
xr = layers.BatchNormalization()(xr)
xr = layers.Activation('relu')(xr)
xr = layers.Conv2D(32, 3)(xr)
xr = layers.BatchNormalization()(xr)
xr = layers.Activation('relu')(xr)
# xr = layers.MaxPooling2D()(xr)
# xc = layers.Conv2D(32, 3, 2)(inpc)
# xc = layers.BatchNormalization()(xc)
# xc = layers.Activation('relu')(xc)
# xc = layers.Conv2D(32, 3)(xc)
# xc = layers.BatchNormalization()(xc)
# xc = layers.Activation('relu')(xc)
# xc = layers.MaxPooling2D()(xc)
# x = layers.concatenate((xl, xc, xr))
x = layers.concatenate([xl, xr])
x = layers.MaxPooling2D()(x)
x = layers.Conv2D(96, 3, strides=(2, 2))(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(128, 3)(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(128, 3)(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D()(x)
x = layers.Conv2D(256, 3)(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(256, 3)(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.MaxPooling2D()(x)
x = layers.Conv2D(512, 3)(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
x = layers.Conv2D(512, 3)(x)
x = layers.BatchNormalization()(x)
x = layers.Activation('relu')(x)
# x = layers.GlobalAveragePooling2D()(x)
x = layers.Flatten()(x)
x = layers.Dense(2, activation='sigmoid')(x)
# model = models.Model([inpl, inpc, inpr], x)
model = models.Model([inpl, inpr], x)
model.compile(optimizer='adam', loss='mae')
return model
def __init__(self, **kwargs):
super(WeightsNormalize, self).__init__(**kwargs)
self.conv = layers.Conv2D(3, kernel_size=1)
self.concat = layers.Concatenate(axis=-1)
self.softmax = layers.Softmax()
def Conv2DClassifierIn1(x_train, y_train, x_test, y_test):
summary = True
verbose = 1
# setHyperParams------------------------------------------------------------------------------------------------
batch_size = {{choice([32, 64, 128, 256, 512])}}
epoch = {{choice([25, 50, 75, 100, 125, 150, 175, 200])}}
conv_block = {{choice(['two', 'three', 'four'])}}
conv1_num = {{choice([8, 16, 32, 64])}}
conv2_num = {{choice([16, 32, 64, 128])}}
conv3_num = {{choice([32, 64, 128])}}
conv4_num = {{choice([32, 64, 128, 256])}}
dense1_num = {{choice([128, 256, 512])}}
dense2_num = {{choice([64, 128, 256])}}
l1_regular_rate = {{uniform(0.00001, 1)}}
l2_regular_rate = {{uniform(0.000001, 1)}}
drop1_num = {{uniform(0.1, 1)}}
drop2_num = {{uniform(0.0001, 1)}}
activator = {{choice(['elu', 'relu', 'tanh'])}}
optimizer = {{choice(['adam', 'rmsprop', 'SGD'])}}
#---------------------------------------------------------------------------------------------------------------
kernel_size = (3, 3)
pool_size = (2, 2)
initializer = 'random_uniform'
padding_style = 'same'
loss_type = 'binary_crossentropy'
metrics = ['accuracy']
my_callback = None
# early_stopping = EarlyStopping(monitor='val_loss', patience=4)
# checkpointer = ModelCheckpoint(filepath='keras_weights.hdf5',
# verbose=1,
# save_best_only=True)
# my_callback = callbacks.ReduceLROnPlateau(monitor='val_loss', factor=0.2,
# patience=5, min_lr=0.0001)
# build --------------------------------------------------------------------------------------------------------
input_layer = Input(shape=x_train.shape[1:])
conv = layers.Conv2D(conv1_num,
kernel_size,
padding=padding_style,
kernel_initializer=initializer,
activation=activator)(input_layer)
conv = layers.Conv2D(conv1_num,
kernel_size,
padding=padding_style,
kernel_initializer=initializer,
activation=activator)(conv)
pool = layers.MaxPooling2D(pool_size, padding=padding_style)(conv)
if conv_block == 'two':
conv = layers.Conv2D(conv2_num,
kernel_size,
padding=padding_style,
kernel_initializer=initializer,
activation=activator)(pool)
conv = layers.Conv2D(conv2_num,
kernel_size,
padding=padding_style,
kernel_initializer=initializer,
activation=activator)(conv)
BatchNorm = layers.BatchNormalization(axis=-1)(conv)
pool = layers.MaxPooling2D(pool_size, padding=padding_style)(BatchNorm)
elif conv_block == 'three':
conv = layers.Conv2D(conv2_num,
kernel_size,
padding=padding_style,
kernel_initializer=initializer,
activation=activator)(pool)
conv = layers.Conv2D(conv2_num,
kernel_size,
padding=padding_style,
kernel_initializer=initializer,
activation=activator)(conv)
BatchNorm = layers.BatchNormalization(axis=-1)(conv)
pool = layers.MaxPooling2D(pool_size, padding=padding_style)(BatchNorm)
conv = layers.Conv2D(conv3_num,
kernel_size,
padding=padding_style,
kernel_initializer=initializer,
activation=activator)(pool)
conv = layers.Conv2D(conv3_num,
kernel_size,
padding=padding_style,
kernel_initializer=initializer,
activation=activator)(conv)
BatchNorm = layers.BatchNormalization(axis=-1)(conv)
pool = layers.MaxPooling2D(pool_size, padding=padding_style)(BatchNorm)
elif conv_block == 'four':
conv = layers.Conv2D(conv2_num,
kernel_size,
padding=padding_style,
kernel_initializer=initializer,
activation=activator)(pool)
conv = layers.Conv2D(conv2_num,
kernel_size,
padding=padding_style,
kernel_initializer=initializer,
activation=activator)(conv)
BatchNorm = layers.BatchNormalization(axis=-1)(conv)
pool = layers.MaxPooling2D(pool_size, padding=padding_style)(BatchNorm)
conv = layers.Conv2D(conv3_num,
kernel_size,
padding=padding_style,
kernel_initializer=initializer,
activation=activator)(pool)
conv = layers.Conv2D(conv3_num,
kernel_size,
padding=padding_style,
kernel_initializer=initializer,
activation=activator)(conv)
BatchNorm = layers.BatchNormalization(axis=-1)(conv)
pool = layers.MaxPooling2D(pool_size, padding=padding_style)(BatchNorm)
conv = layers.Conv2D(conv4_num,
kernel_size,
padding=padding_style,
kernel_initializer=initializer,
activation=activator)(pool)
conv = layers.Conv2D(conv4_num,
kernel_size,
padding=padding_style,
kernel_initializer=initializer,
activation=activator)(conv)
BatchNorm = layers.BatchNormalization(axis=-1)(conv)
pool = layers.MaxPooling2D(pool_size, padding=padding_style)(BatchNorm)
flat = layers.Flatten()(pool)
drop = layers.Dropout(drop1_num)(flat)
dense = layers.Dense(dense1_num,
activation=activator,
kernel_regularizer=regularizers.l1_l2(
l1=l1_regular_rate, l2=l2_regular_rate))(drop)
BatchNorm = layers.BatchNormalization(axis=-1)(dense)
drop = layers.Dropout(drop2_num)(BatchNorm)
dense = layers.Dense(dense2_num,
activation=activator,
kernel_regularizer=regularizers.l1_l2(
l1=l1_regular_rate, l2=l2_regular_rate))(drop)
output_layer = layers.Dense(len(np.unique(y_train)),
activation='softmax')(dense)
model = models.Model(inputs=input_layer, outputs=output_layer)
if summary:
model.summary()
# train(self):
class_weights = class_weight.compute_class_weight('balanced',
np.unique(y_train),
y_train.reshape(-1))
class_weights_dict = dict(enumerate(class_weights))
model.compile(
optimizer=optimizer,
loss=loss_type,
metrics=metrics # accuracy
)
result = model.fit(x=x_train,
y=y_train,
batch_size=batch_size,
epochs=epoch,
verbose=verbose,
callbacks=my_callback,
validation_data=(x_test, y_test),
shuffle=True,
class_weight=class_weights_dict)
validation_acc = np.amax(result.history['val_acc'])
print('Best validation acc of epoch:', validation_acc)
return {'loss': -validation_acc, 'status': STATUS_OK, 'model': model}
def test_TimeDistributed():
# first, test with Dense layer
model = Sequential()
model.add(wrappers.TimeDistributed(layers.Dense(2), input_shape=(3, 4)))
model.add(layers.Activation('relu'))
model.compile(optimizer='rmsprop', loss='mse')
model.fit(np.random.random((10, 3, 4)),
np.random.random((10, 3, 2)),
epochs=1,
batch_size=10)
# test config
model.get_config()
# test when specifying a batch_input_shape
test_input = np.random.random((1, 3, 4))
test_output = model.predict(test_input)
weights = model.layers[0].get_weights()
reference = Sequential()
reference.add(
wrappers.TimeDistributed(layers.Dense(2), batch_input_shape=(1, 3, 4)))
reference.add(layers.Activation('relu'))
reference.compile(optimizer='rmsprop', loss='mse')
reference.layers[0].set_weights(weights)
reference_output = reference.predict(test_input)
assert_allclose(test_output, reference_output, atol=1e-05)
# test with Embedding
model = Sequential()
model.add(
wrappers.TimeDistributed(layers.Embedding(5, 6),
batch_input_shape=(10, 3, 4),
dtype='int32'))
model.compile(optimizer='rmsprop', loss='mse')
model.fit(np.random.randint(5, size=(10, 3, 4), dtype='int32'),
np.random.random((10, 3, 4, 6)),
epochs=1,
batch_size=10)
# compare to not using batch_input_shape
test_input = np.random.randint(5, size=(10, 3, 4), dtype='int32')
test_output = model.predict(test_input)
weights = model.layers[0].get_weights()
reference = Sequential()
reference.add(
wrappers.TimeDistributed(layers.Embedding(5, 6),
input_shape=(3, 4),
dtype='int32'))
reference.compile(optimizer='rmsprop', loss='mse')
reference.layers[0].set_weights(weights)
reference_output = reference.predict(test_input)
assert_allclose(test_output, reference_output, atol=1e-05)
# test with Conv2D
model = Sequential()
model.add(
wrappers.TimeDistributed(layers.Conv2D(5, (2, 2), padding='same'),
input_shape=(2, 4, 4, 3)))
model.add(layers.Activation('relu'))
model.compile(optimizer='rmsprop', loss='mse')
model.train_on_batch(np.random.random((1, 2, 4, 4, 3)),
np.random.random((1, 2, 4, 4, 5)))
model = model_from_json(model.to_json())
model.summary()
# test stacked layers
model = Sequential()
model.add(wrappers.TimeDistributed(layers.Dense(2), input_shape=(3, 4)))
model.add(wrappers.TimeDistributed(layers.Dense(3)))
model.add(layers.Activation('relu'))
model.compile(optimizer='rmsprop', loss='mse')
model.fit(np.random.random((10, 3, 4)),
np.random.random((10, 3, 3)),
epochs=1,
batch_size=10)
# test wrapping Sequential model
model = Sequential()
model.add(layers.Dense(3, input_dim=2))
outer_model = Sequential()
outer_model.add(wrappers.TimeDistributed(model, input_shape=(3, 2)))
outer_model.compile(optimizer='rmsprop', loss='mse')
outer_model.fit(np.random.random((10, 3, 2)),
np.random.random((10, 3, 3)),
epochs=1,
batch_size=10)
# test with functional API
x = Input(shape=(3, 2))
y = wrappers.TimeDistributed(model)(x)
outer_model = Model(x, y)
outer_model.compile(optimizer='rmsprop', loss='mse')
outer_model.fit(np.random.random((10, 3, 2)),
np.random.random((10, 3, 3)),
epochs=1,
batch_size=10)
# test with BatchNormalization
model = Sequential()
model.add(
wrappers.TimeDistributed(layers.BatchNormalization(center=True,
scale=True),
name='bn',
input_shape=(10, 2)))
model.compile(optimizer='rmsprop', loss='mse')
# Assert that mean and variance are 0 and 1.
td = model.layers[0]
assert np.array_equal(td.get_weights()[2], np.array([0, 0]))
assert np.array_equal(td.get_weights()[3], np.array([1, 1]))
# Train
model.train_on_batch(np.random.normal(loc=2, scale=2, size=(1, 10, 2)),
np.broadcast_to(np.array([0, 1]), (1, 10, 2)))
# Assert that mean and variance changed.
assert not np.array_equal(td.get_weights()[2], np.array([0, 0]))
assert not np.array_equal(td.get_weights()[3], np.array([1, 1]))
# Verify input_map has one mapping from inputs to reshaped inputs.
uid = object_list_uid(model.inputs)
assert len(td._input_map.keys()) == 1
assert uid in td._input_map
assert K.int_shape(td._input_map[uid]) == (None, 2)
def build_model():
from keras import models, layers
# Build U-Net model
def upsample_conv(filters, kernel_size, strides, padding):
return layers.Conv2DTranspose(filters, kernel_size, strides=strides, padding=padding)
def upsample_simple(filters, kernel_size, strides, padding):
return layers.UpSampling2D(strides)
if UPSAMPLE_MODE=='DECONV':
upsample=upsample_conv
else:
upsample=upsample_simple
input_img = layers.Input(t_x.shape[1:], name = 'RGB_Input')
pp_in_layer = input_img
if NET_SCALING is not None:
pp_in_layer = layers.AvgPool2D(NET_SCALING)(pp_in_layer)
pp_in_layer = layers.GaussianNoise(GAUSSIAN_NOISE)(pp_in_layer)
pp_in_layer = layers.BatchNormalization()(pp_in_layer)
c1 = layers.Conv2D(8, (3, 3), activation='relu', padding='same') (pp_in_layer)
c1 = layers.Conv2D(8, (3, 3), activation='relu', padding='same') (c1)
p1 = layers.MaxPooling2D((2, 2)) (c1)
c2 = layers.Conv2D(16, (3, 3), activation='relu', padding='same') (p1)
c2 = layers.Conv2D(16, (3, 3), activation='relu', padding='same') (c2)
p2 = layers.MaxPooling2D((2, 2)) (c2)
c3 = layers.Conv2D(32, (3, 3), activation='relu', padding='same') (p2)
c3 = layers.Conv2D(32, (3, 3), activation='relu', padding='same') (c3)
p3 = layers.MaxPooling2D((2, 2)) (c3)
c4 = layers.Conv2D(64, (3, 3), activation='relu', padding='same') (p3)
c4 = layers.Conv2D(64, (3, 3), activation='relu', padding='same') (c4)
p4 = layers.MaxPooling2D(pool_size=(2, 2)) (c4)
c5 = layers.Conv2D(128, (3, 3), activation='relu', padding='same') (p4)
c5 = layers.Conv2D(128, (3, 3), activation='relu', padding='same') (c5)
u6 = upsample(64, (2, 2), strides=(2, 2), padding='same') (c5)
u6 = layers.concatenate([u6, c4])
c6 = layers.Conv2D(64, (3, 3), activation='relu', padding='same') (u6)
c6 = layers.Conv2D(64, (3, 3), activation='relu', padding='same') (c6)
u7 = upsample(32, (2, 2), strides=(2, 2), padding='same') (c6)
u7 = layers.concatenate([u7, c3])
c7 = layers.Conv2D(32, (3, 3), activation='relu', padding='same') (u7)
c7 = layers.Conv2D(32, (3, 3), activation='relu', padding='same') (c7)
u8 = upsample(16, (2, 2), strides=(2, 2), padding='same') (c7)
u8 = layers.concatenate([u8, c2])
c8 = layers.Conv2D(16, (3, 3), activation='relu', padding='same') (u8)
c8 = layers.Conv2D(16, (3, 3), activation='relu', padding='same') (c8)
u9 = upsample(8, (2, 2), strides=(2, 2), padding='same') (c8)
u9 = layers.concatenate([u9, c1], axis=3)
c9 = layers.Conv2D(8, (3, 3), activation='relu', padding='same') (u9)
c9 = layers.Conv2D(8, (3, 3), activation='relu', padding='same') (c9)
d = layers.Conv2D(1, (1, 1), activation='sigmoid') (c9)
# d = layers.Cropping2D((EDGE_CROP, EDGE_CROP))(d)
# d = layers.ZeroPadding2D((EDGE_CROP, EDGE_CROP))(d)
if NET_SCALING is not None:
d = layers.UpSampling2D(NET_SCALING)(d)
seg_model = models.Model(inputs=[input_img], outputs=[d])
seg_model.summary()
def model_U_VGG_Centerline_Localheight():
# input_shape = (720, 1280, 3)
# input_shape = (512,512,3)
input_shape = (None, None, 3)
inputs = Input(shape=input_shape, name='input')
# Block 1
x0 = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv1')(inputs)
x0 = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='block1_conv2')(x0)
x0 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x0)
# Block 2
x1 = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv1')(x0)
x1 = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='block2_conv2')(x1)
x1 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x1)
# Block 3
x2 = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv1')(x1)
x2 = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv2')(x2)
x2_take = layers.Conv2D(256, (3, 3),
activation='relu',
padding='same',
name='block3_conv3')(x2)
x2 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x2_take)
# Block 4
x3 = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv1')(x2)
x3 = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv2')(x3)
x3_take = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block4_conv3')(x3)
x3 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block4_pool')(x3_take)
# Block 5
x4 = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv1')(x3)
x4 = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv2')(x4)
x4_take = layers.Conv2D(512, (3, 3),
activation='relu',
padding='same',
name='block5_conv3')(x4)
x4 = layers.MaxPooling2D((2, 2), strides=(2, 2), name='block5_pool')(x4_take)
# f1 = UpSampling2D((2,2))(x4)
# if TASK_4:
# f1 = ZeroPadding2D(padding=((1,0), (0,0)), name = 'f1')(f1)
f1 = x4_take
f2 = x3
h1 = Concatenate()([f2, f1])
h1 = layers.Conv2D(128, (1, 1),
activation='relu',
padding='same',
name='up1_1')(h1)
h1 = layers.Conv2D(128, (3, 3),
activation='relu',
padding='same',
name='up1_2')(h1)
h2 = Concatenate()([x2, UpSampling2D((2, 2))(h1)])
h2 = layers.Conv2D(64, (1, 1),
activation='relu',
padding='same',
name='up2_1')(h2)
h2 = layers.Conv2D(64, (3, 3),
activation='relu',
padding='same',
name='up2_2')(h2)
h3 = Concatenate()([x1, UpSampling2D((2, 2))(h2)])
h3 = layers.Conv2D(32, (1, 1),
activation='relu',
padding='same',
name='up3_1')(h3)
h3 = layers.Conv2D(32, (3, 3),
activation='relu',
padding='same',
name='up3_2')(h3)
h4_take = Concatenate()([x0, UpSampling2D((2, 2))(h3)])
h4 = layers.Conv2D(32, (1, 1),
activation='relu',
padding='same',
name='up4_1')(h4_take)
h4 = layers.Conv2D(32, (3, 3),
activation='relu',
padding='same',
name='up4_2')(h4)
h5 = Concatenate()([inputs, UpSampling2D((2, 2))(h4)])
h5 = layers.Conv2D(16, (1, 1),
activation='relu',
padding='same',
name='up5_1')(h5)
################## output for TEXT/NON-TEXT ############
o1 = layers.Conv2D(3, (3, 3),
activation='softmax',
padding='same',
name='up5_2')(h5)
################## output for centerline /other ###########
h41 = layers.Conv2D(32, (1, 1),
activation='relu',
padding='same',
name='up41_1')(h4_take)
h41 = layers.Conv2D(32, (3, 3),
activation='relu',
padding='same',
name='up41_2')(h41)
h51 = Concatenate()([inputs, UpSampling2D((2, 2))(h41)])
h51 = layers.Conv2D(16, (1, 1),
activation='relu',
padding='same',
name='up51_1')(h51)
o11 = layers.Conv2D(2, (3, 3),
activation='softmax',
padding='same',
name='up51_2')(h51)
################ Regression ###########################
'''
b1 = Concatenate(name='agg_feat-1')([x4_take, h1]) # block_conv3, up1_2 # 32,32,630
b1 = layers.Conv2DTranspose(128, (3, 3), strides=(2, 2), padding='same',
activation='relu', name='agg_feat-2')(b1) # 64,64,128
'''
# ------ local height regression ------
h42 = layers.Conv2D(32, (1, 1),
activation='relu',
padding='same',
name='up42_1')(h4_take)
h42 = layers.Conv2D(32, (3, 3),
activation='relu',
padding='same',
name='up42_2')(h42)
h52 = Concatenate()([inputs, UpSampling2D((2, 2))(h42)])
h52 = layers.Conv2D(16, (1, 1),
activation='relu',
padding='same',
name='up52_1')(h52)
o5 = layers.Conv2D(1, (3, 3),
activation='relu',
padding='same',
name='regress-4-1')(h52)
# o1: t/nt, o11:centerline, o2:x,y, o3:sin,cos, o4:bounding box width,height, o5:localheight
# model = Model(inputs, [o1,o11, o2,o3,o4], name = 'U-VGG-model')
model = Model(inputs, [o1, o11, o5], name='U-VGG-model-Localheight')
return model
def create_instance_head(self, num_classes, rois, train_bn=True):
"""
instance segmentation mask head
Params
ROIs: [batch, num_rois, H, W, C], the C of the initial Rois may not identical
so it needs a preprocess first.
"""
feature_maps = []
for i in range(len(self.layers)):
feature_maps.append(self.outputs[self.layers[i]])
x = PyramidROIExtract([args.det_kernel, args.det_kernel],
self.config,
name="roi_align_mask")([rois] + feature_maps)
# test_x = tf.random_uniform((16, 700, 3, 3, 512))
# print('test x shape: ', test_x.shape, tf.shape(test_x))
# print('test x: ', test_x)
# print('x shape: ', x.shape, tf.shape(x))
# print('x: ', x)
# t1 = KL.Conv2D(256, (3, 3), padding="same")(x[:, 0, :, :, :])
# print(t1)
# t2 = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"),
# name="test_mask_test")(x)
# print(t2)
# t_x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"),
# name="test_mask_conv1")(test_x)
with tf.variable_scope(DEFAULT_SSD_SCOPE) as sc:
feature_maps = []
for i in range(len(self.layers)):
feature_maps.append(self.outputs[self.layers[i]])
x = PyramidROIExtract([args.det_kernel, args.det_kernel],
self.config,
name="roi_align_mask")([rois] + feature_maps)
x = KL.TimeDistributed(KL.Conv2DTranspose(256, (2, 2),
strides=2,
activation="relu"),
name="instance_mask_deconv1")(
x[:, :, :, :, :])
# Conv layers
x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"),
name="instance_mask_conv1")(x)
x = KL.TimeDistributed(BatchNorm(),
name='instance_mask_bn1')(x,
training=train_bn)
x = KL.Activation('relu')(x)
x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"),
name="instance_mask_conv2")(x)
x = KL.TimeDistributed(BatchNorm(),
name='instance_mask_bn2')(x,
training=train_bn)
x = KL.Activation('relu')(x)
x = KL.TimeDistributed(KL.Conv2DTranspose(256, (2, 2),
strides=2,
activation="relu"),
name="instance_mask_deconv2")(x)
x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"),
name="instance_mask_conv3")(x)
x = KL.TimeDistributed(BatchNorm(),
name='instance_mask_bn3')(x,
training=train_bn)
x = KL.Activation('relu')(x)
x = KL.TimeDistributed(KL.Conv2D(256, (3, 3), padding="same"),
name="instance_mask_conv4")(x)
x = KL.TimeDistributed(BatchNorm(),
name='instance_mask_bn4')(x,
training=train_bn)
x = KL.Activation('relu')(x)
x = KL.TimeDistributed(KL.Conv2DTranspose(256, (2, 2),
strides=2,
activation="relu"),
name="instance_mask_deconv2")(x)
x = KL.TimeDistributed(KL.Conv2D(num_classes, (1, 1),
strides=1,
activation="sigmoid"),
name="instance_mask")(x)
return x
def train():
############### 1. Generating captchas
images, labels = get_data_set(width=img_x, height=img_y, nr_of_chars=nr_of_chars, color=color,
nr_of_captchas=numberOfCaptchas, numbers=numbers)
############### 2. Preprocessing the data
#
# Data needs to be reshaped into a 4D tensor - (sample_number, x_img_size, y_img_size, num_channels)
# The number of channels = number of colors grescale = 1, color = 3
images_train = images[:testSize]
print('Before reshaping, 3D :', images_train.shape)
images_train = images_train.reshape(images_train.shape[0], img_x, img_y, depth)
images_train = images_train.astype('float32') / 255 # Scaling color dimension to 0-255 to 0-1
print('After reshaping, 3D :', images_train[0].shape)
images_test = images[testSize:]
images_test = images_test.reshape(images_test.shape[0], img_x, img_y, depth)
images_test = images_test.astype('float32') / 255
labels_train = labels[:testSize]
labels_test = labels[testSize:]
# The categories are characters [aa, ab, ac, ... ]
categories = get_all_possible_label_categories(nr_of_chars)
lb = LabelBinarizer().fit(categories)
labels_encoded_train = lb.transform(labels_train)
labels_encoded_test = lb.transform(labels_test)
############### 3. Building the neural network
# Sequential model
model = Sequential()
# First conv: 32 filters of 3x3
model.add(Conv2D(32, (5, 5), padding='same', input_shape=(img_x, img_y, 1), activation="relu"))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Second conv
model.add(layers.Conv2D(64, (5, 5), padding='same', activation='relu'))
#model.add(Dropout(0.3))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Third conv
model.add(layers.Conv2D(96, (5, 5), padding='same', activation='relu'))
#model.add(Dropout(0.3))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Fourth conv
model.add(layers.Conv2D(128, (5, 5), padding='same', activation='relu'))
#model.add(Dropout(0.3))
model.add(MaxPooling2D(pool_size=(2, 2), strides=(2, 2)))
# Fully connected layer
model.add(Flatten())
model.add(Dense(1024, activation="relu"))
model.add(Dropout(0.5))
model.add(Dense(len(categories), activation="softmax"))
print(model.summary())
model.compile(loss="categorical_crossentropy", optimizer=optimizers.Adam(), metrics=["accuracy"])
############### 4. Training the neural network
history = model.fit(
images_train,
labels_encoded_train,
batch_size=128,
validation_data=(images_test, labels_encoded_test),
epochs=80
)
############### 5. Visualizing and saving the result
# Retrieving the acc and loss
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']
# Creating the output dir for this model
model_dir = os.path.join(output_dir, 'model_chars' + str(nr_of_chars) + '_acc_' + str(max(val_acc)).replace('.', '_')[:4])
if os.path.exists(model_dir):
model_dir = os.path.join(model_dir, '_' + str(time.time()))
os.mkdir(model_dir)
model.save(os.path.join(model_dir, 'model.h5'))
model_json = model.to_json()
copyfile('./captcha_learner_1.py', os.path.join(model_dir,'captcha_learner_1.py'))
with open(os.path.join(model_dir, 'model.json'), "w") as json_file:
json_file.write(model_json)
epochs = range(1, len(acc) + 1)
plt.figure()
plt.subplot(211)
plt.plot(epochs, acc, 'bo', label='Training acc')
plt.plot(epochs, val_acc, 'b', label='Validation acc')
plt.title('Training and validation accuracy')
plt.legend()
plt.subplot(212)
plt.plot(epochs, loss, 'bo', label='Training loss')
plt.plot(epochs, val_loss, 'b', label='Validation loss')
plt.title('Training and validation loss')
plt.legend()
plt.savefig(os.path.join(model_dir, 'metrics.png'))
plt.show()
