File size: 3,582 Bytes
9ba9ac1 |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 |
from tensorflow.data import Dataset
import tensorflow.keras as keras
from tensorflow.keras.optimizers import Adam
from tensorflow.keras.layers import (
Conv2D,
Input,
MaxPooling2D,
Dense,
Dropout,
MaxPool1D,
Flatten,
AveragePooling1D,
BatchNormalization,
)
from tensorflow.keras import Model
import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.models import Model
from tensorflow.keras.layers import Input, Add, Activation, Dropout, Flatten, Dense
from tensorflow.keras.layers import Convolution2D, MaxPooling2D, AveragePooling2D
from tensorflow.keras.layers import BatchNormalization
from tensorflow.keras.regularizers import l2
from tensorflow.keras import backend as K
from tensorflow.keras.optimizers import SGD
import warnings
warnings.filterwarnings("ignore")
def basemodel(weight_decay):
# 2 hidden layers
model_input = Input(
shape=(
32,
32,
1,
)
)
model = Conv2D(
32,
kernel_size=(3, 3),
kernel_regularizer=l2(weight_decay),
activation="relu",
)(model_input)
model = Conv2D(
64, kernel_size=(3, 3), kernel_regularizer=l2(weight_decay), activation="relu"
)(model)
model = MaxPooling2D(pool_size=(2, 2))(model)
model = BatchNormalization()(model)
model = Flatten()(model)
model = Dense(4, kernel_regularizer=l2(weight_decay), activation="softmax")(model)
model = Model(inputs=model_input, outputs=model)
return model
def model_2(weight_decay):
model_input = Input(
shape=(
32,
32,
1,
)
)
model = Conv2D(
32,
kernel_size=(3, 3),
kernel_regularizer=l2(weight_decay),
activation="relu",
)(model_input)
model = Conv2D(
64, kernel_size=(3, 3), kernel_regularizer=l2(weight_decay), activation="relu"
)(model)
model = MaxPooling2D(pool_size=(2, 2))(model)
model = BatchNormalization()(model)
model = Conv2D(
128, kernel_size=(3, 3), kernel_regularizer=l2(weight_decay), activation="relu"
)(model)
model = MaxPooling2D(pool_size=(2, 2))(model)
model = BatchNormalization()(model)
model = Flatten()(model)
model = Dense(4, kernel_regularizer=l2(weight_decay), activation="softmax")(model)
model = Model(inputs=model_input, outputs=model)
return model
def model_3(weight_decay):
# 4 hidden layers
model_input = Input(
shape=(
32,
32,
1,
)
)
model = Conv2D(
32,
kernel_size=(3, 3),
kernel_regularizer=l2(weight_decay),
activation="relu",
)(model_input)
model = Conv2D(
64, kernel_size=(3, 3), kernel_regularizer=l2(weight_decay), activation="relu"
)(model)
model = MaxPooling2D(pool_size=(2, 2))(model)
model = BatchNormalization()(model)
model = Conv2D(
128, kernel_size=(3, 3), kernel_regularizer=l2(weight_decay), activation="relu"
)(model)
model = MaxPooling2D(pool_size=(2, 2))(model)
model = BatchNormalization()(model)
model = Conv2D(
256, kernel_size=(3, 3), kernel_regularizer=l2(weight_decay), activation="relu"
)(model)
model = MaxPooling2D(pool_size=(2, 2))(model)
model = BatchNormalization()(model)
model = Flatten()(model)
model = Dense(4, kernel_regularizer=l2(weight_decay), activation="softmax")(model)
model = Model(inputs=model_input, outputs=model)
return model
|