mirror of
https://github.com/AntoineHX/smart_augmentation.git
synced 2025-05-04 20:20:46 +02:00
131 lines
5.9 KiB
Python
131 lines
5.9 KiB
Python
|
import matplotlib.pyplot as plt
|
||
|
|
from far_ho.examples.datasets import Datasets, Dataset
|
||
|
|
|
||
|
|
import os
|
||
|
|
import numpy as np
|
||
|
|
import tensorflow as tf
|
||
|
|
|
||
|
|
import augmentation_transforms as augmentation_transforms ##### ATTENTION FICHIER EN DOUBLE => A REGLER MIEUX ####
|
||
|
|
|
||
|
|
def viz_data(dataset, fig_name='data_sample',aug_policy=None):
|
||
|
|
|
||
|
|
plt.figure(figsize=(10,10))
|
||
|
|
for i in range(25):
|
||
|
|
plt.subplot(5,5,i+1)
|
||
|
|
plt.xticks([])
|
||
|
|
plt.yticks([])
|
||
|
|
plt.grid(False)
|
||
|
|
|
||
|
|
img = dataset.data[i][:,:,0]
|
||
|
|
if aug_policy :
|
||
|
|
img = augment_img(img,aug_policy)
|
||
|
|
#print('im shape',img.shape)
|
||
|
|
plt.imshow(img, cmap=plt.cm.binary)
|
||
|
|
plt.xlabel(np.nonzero(dataset.target[i])[0].item())
|
||
|
|
|
||
|
|
plt.savefig(fig_name)
|
||
|
|
|
||
|
|
def augment_img(data, policy):
|
||
|
|
|
||
|
|
#print('Im shape',data.shape)
|
||
|
|
data = np.stack((data,)*3, axis=-1) #BOF BOF juste pour forcer 3 channels
|
||
|
|
#print('Im shape',data.shape)
|
||
|
|
final_img = augmentation_transforms.apply_policy(policy, data)
|
||
|
|
#final_img = augmentation_transforms.random_flip(augmentation_transforms.zero_pad_and_crop(final_img, 4))
|
||
|
|
# Apply cutout
|
||
|
|
#final_img = augmentation_transforms.cutout_numpy(final_img)
|
||
|
|
|
||
|
|
im_rgb = np.array(final_img, np.float32)
|
||
|
|
im_gray = np.dot(im_rgb[...,:3], [0.2989, 0.5870, 0.1140]) #Just pour retourner a 1 channel
|
||
|
|
|
||
|
|
return im_gray
|
||
|
|
|
||
|
|
|
||
|
|
### https://www.kaggle.com/raoulma/mnist-image-class-tensorflow-cnn-99-51-test-acc#5.-Build-the-neural-network-with-tensorflow-
|
||
|
|
## build the neural network class
|
||
|
|
# weight initialization
|
||
|
|
def weight_variable(shape, name = None):
|
||
|
|
initial = tf.truncated_normal(shape, stddev=0.1)
|
||
|
|
return tf.Variable(initial, name = name)
|
||
|
|
|
||
|
|
# bias initialization
|
||
|
|
def bias_variable(shape, name = None):
|
||
|
|
initial = tf.constant(0.1, shape=shape) # positive bias
|
||
|
|
return tf.Variable(initial, name = name)
|
||
|
|
|
||
|
|
# 2D convolution
|
||
|
|
def conv2d(x, W, name = None):
|
||
|
|
return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding='SAME', name = name)
|
||
|
|
|
||
|
|
# max pooling
|
||
|
|
def max_pool_2x2(x, name = None):
|
||
|
|
return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1],
|
||
|
|
padding='SAME', name = name)
|
||
|
|
|
||
|
|
def cnn(x_data_tf,y_data_tf, name='model'):
|
||
|
|
# tunable hyperparameters for nn architecture
|
||
|
|
s_f_conv1 = 3; # filter size of first convolution layer (default = 3)
|
||
|
|
n_f_conv1 = 36; # number of features of first convolution layer (default = 36)
|
||
|
|
s_f_conv2 = 3; # filter size of second convolution layer (default = 3)
|
||
|
|
n_f_conv2 = 36; # number of features of second convolution layer (default = 36)
|
||
|
|
s_f_conv3 = 3; # filter size of third convolution layer (default = 3)
|
||
|
|
n_f_conv3 = 36; # number of features of third convolution layer (default = 36)
|
||
|
|
n_n_fc1 = 576; # number of neurons of first fully connected layer (default = 576)
|
||
|
|
|
||
|
|
# 1.layer: convolution + max pooling
|
||
|
|
W_conv1_tf = weight_variable([s_f_conv1, s_f_conv1, 1, n_f_conv1], name = 'W_conv1_tf') # (5,5,1,32)
|
||
|
|
b_conv1_tf = bias_variable([n_f_conv1], name = 'b_conv1_tf') # (32)
|
||
|
|
h_conv1_tf = tf.nn.relu(conv2d(x_data_tf,
|
||
|
|
W_conv1_tf) + b_conv1_tf,
|
||
|
|
name = 'h_conv1_tf') # (.,28,28,32)
|
||
|
|
h_pool1_tf = max_pool_2x2(h_conv1_tf,
|
||
|
|
name = 'h_pool1_tf') # (.,14,14,32)
|
||
|
|
|
||
|
|
# 2.layer: convolution + max pooling
|
||
|
|
W_conv2_tf = weight_variable([s_f_conv2, s_f_conv2,
|
||
|
|
n_f_conv1, n_f_conv2],
|
||
|
|
name = 'W_conv2_tf')
|
||
|
|
b_conv2_tf = bias_variable([n_f_conv2], name = 'b_conv2_tf')
|
||
|
|
h_conv2_tf = tf.nn.relu(conv2d(h_pool1_tf,
|
||
|
|
W_conv2_tf) + b_conv2_tf,
|
||
|
|
name ='h_conv2_tf') #(.,14,14,32)
|
||
|
|
h_pool2_tf = max_pool_2x2(h_conv2_tf, name = 'h_pool2_tf') #(.,7,7,32)
|
||
|
|
|
||
|
|
# 3.layer: convolution + max pooling
|
||
|
|
W_conv3_tf = weight_variable([s_f_conv3, s_f_conv3,
|
||
|
|
n_f_conv2, n_f_conv3],
|
||
|
|
name = 'W_conv3_tf')
|
||
|
|
b_conv3_tf = bias_variable([n_f_conv3], name = 'b_conv3_tf')
|
||
|
|
h_conv3_tf = tf.nn.relu(conv2d(h_pool2_tf,
|
||
|
|
W_conv3_tf) + b_conv3_tf,
|
||
|
|
name = 'h_conv3_tf') #(.,7,7,32)
|
||
|
|
h_pool3_tf = max_pool_2x2(h_conv3_tf,
|
||
|
|
name = 'h_pool3_tf') # (.,4,4,32)
|
||
|
|
|
||
|
|
# 4.layer: fully connected
|
||
|
|
W_fc1_tf = weight_variable([4*4*n_f_conv3,n_n_fc1],
|
||
|
|
name = 'W_fc1_tf') # (4*4*32, 1024)
|
||
|
|
b_fc1_tf = bias_variable([n_n_fc1], name = 'b_fc1_tf') # (1024)
|
||
|
|
h_pool3_flat_tf = tf.reshape(h_pool3_tf, [-1,4*4*n_f_conv3],
|
||
|
|
name = 'h_pool3_flat_tf') # (.,1024)
|
||
|
|
h_fc1_tf = tf.nn.relu(tf.matmul(h_pool3_flat_tf,
|
||
|
|
W_fc1_tf) + b_fc1_tf,
|
||
|
|
name = 'h_fc1_tf') # (.,1024)
|
||
|
|
|
||
|
|
# add dropout
|
||
|
|
#keep_prob_tf = tf.placeholder(dtype=tf.float32, name = 'keep_prob_tf')
|
||
|
|
#h_fc1_drop_tf = tf.nn.dropout(h_fc1_tf, keep_prob_tf, name = 'h_fc1_drop_tf')
|
||
|
|
|
||
|
|
# 5.layer: fully connected
|
||
|
|
W_fc2_tf = weight_variable([n_n_fc1, 10], name = 'W_fc2_tf')
|
||
|
|
b_fc2_tf = bias_variable([10], name = 'b_fc2_tf')
|
||
|
|
z_pred_tf = tf.add(tf.matmul(h_fc1_tf, W_fc2_tf),
|
||
|
|
b_fc2_tf, name = 'z_pred_tf')# => (.,10)
|
||
|
|
# predicted probabilities in one-hot encoding
|
||
|
|
y_pred_proba_tf = tf.nn.softmax(z_pred_tf, name='y_pred_proba_tf')
|
||
|
|
|
||
|
|
# tensor of correct predictions
|
||
|
|
y_pred_correct_tf = tf.equal(tf.argmax(y_pred_proba_tf, 1),
|
||
|
|
tf.argmax(y_data_tf, 1),
|
||
|
|
name = 'y_pred_correct_tf')
|
||
|
|
return y_pred_proba_tf
|