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test_vgg16.py
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from keras.applications.vgg16 import VGG16
from keras.preprocessing import image
from keras.applications.vgg16 import preprocess_input
from keras.models import Model
import numpy as np
import os
from utils_laj import cache
from utils_laj import do_PCA
from utils_laj import plot_scatter
import matplotlib.pyplot as plt
from sklearn.cluster import KMeans
from sklearn.cluster import DBSCAN
model = VGG16(weights='imagenet')
model = Model(inputs=model.input, outputs=model.get_layer('fc2').output)
# img_path = r'D:\LAHIRU\Work\Toilet\transferlearning_ResNet_VGG16\data\cats\cat.26.jpg'
# img = image.load_img(img_path, target_size=(224, 224))
# x = image.img_to_array(img)
# x = np.expand_dims(x, axis=0)
# x = preprocess_input(x)
# features = np.array(model.predict(x))
# print(features,features.shape)
# data_path = r'D:\LAHIRU\Work\Toilet\inceptionV3_custom\Urinal_images\New folder'
# data_path = r'D:\LAHIRU\Work\Toilet\InceptionV3_model_toilet_confusion_metrix\test\New folder'
# data_dir_list = os.listdir(data_path)
# print(data_dir_list)
# img_data_list = []
# data_per_cls = []
# for dataset in data_dir_list:
# img_list = os.listdir(data_path + '/' + dataset)
# data_per_cls.append(len(img_list))
# print('Loaded the images of dataset-' + '{}\n'.format(dataset))
# for img in img_list:
# img_path = data_path + '/' + dataset + '/' + img
# img = image.load_img(img_path, target_size=(224, 224))
# x = image.img_to_array(img)
# # print('img_to_array', x.shape)
# x = np.expand_dims(x, axis=0)
# # print('expand_dim',x.shape)
# x = preprocess_input(x)
# # x = x/255
# # print('Input image shape:', x.shape)
# img_data_list.append(x)
#
# img_data = np.array(img_data_list)
# # img_data = img_data.astype('float32')
# print(img_data.shape)
# img_data = np.rollaxis(img_data, 1, 0)
# print(img_data.shape)
# img_data = img_data[0]
# print(img_data.shape)
#
# # Define the number of classes
# num_classes = len(data_dir_list)
# num_of_samples = img_data.shape[0]
# labels = np.ones((num_of_samples,), dtype='int64')
# print(data_per_cls)
# j=0
# k=0
# for i in data_per_cls:
# labels[j:i+j] = k
# print(j,i+j)
# j = j+i
# k = k+1
x=cache('toilet_images.pkl')
# x=cache(r'D:\LAHIRU\Work\Toilet\transferlearning_ResNet_VGG16\save\vgg_16\testing\testing.pkl',img_data)
# cls = cache(r'D:\LAHIRU\Work\Toilet\transferlearning_ResNet_VGG16\save\vgg_16\testing\testing_cls.pkl',labels)
cls = cache('toilet_label.pkl')
features = np.array(model.predict(x))
features = cache('./save/vgg_16/features-fc2.pkl',features)
# features = cache('./save/vgg_16/testing/features.pkl')
print(features,features.shape)
Z,label_x,label_y,label_z=do_PCA(features,cls,'PCA analysis of extracted features\nDCNN = VGG-16 , Layer = FC-2')
# np.random.seed(42)
# k_cls = 3
# cluster = 'kmeans'
# if cluster == 'kmeans':
# # kmeans = KMeans(n_clusters=k_cls,init='k-means++',n_init=k_cls)
# kmeans = KMeans(n_clusters=k_cls)
# kmeans.fit(Z)
# labels = kmeans.labels_
# elif cluster == 'dbscan':
# db = DBSCAN(eps=1, min_samples=10).fit(Z)
# labels = db.labels_
# plot_scatter(Z, labels, 3, 'Before training\nTransfer values PCA analysis',label_x,label_y,label_z)
plt.show()
from get_csv_data import HandleData
import tensorflow as tf
cls_train = cls
images_train = features
data_set = HandleData(3)
data_set.total_data=227
data_set.data_set=images_train
data_set.label_set=cls_train
learning_rate = 0.01
num_steps = 1000
batch_size = 128
display_step = 100
hidden_size = 1024
input_size = 4096
_SAVEFLAG = 0
_TRAINING = 0
_LOADDIR = './save/vgg_16/'
x = tf.placeholder(tf.float32, shape=[None, input_size], name='x')
y_true = tf.placeholder(tf.float32, shape=[None, 3], name='y_true')
keep_prob = tf.placeholder(tf.float32)
# y_true_cls = tf.argmax(y_true, dimension=1)
weights = {
'h1': tf.Variable(tf.random_normal([input_size, hidden_size])),
'out': tf.Variable(tf.random_normal([hidden_size, 3]))
}
biases = {
'b1': tf.Variable(tf.random_normal([hidden_size])),
'out': tf.Variable(tf.random_normal([3]))
}
def neural_net(x):
# Hidden fully connected layer with 256 neurons
layer_1 = tf.nn.relu(tf.add(tf.matmul(x, weights['h1']), biases['b1']))
layer_1_drop = tf.nn.dropout(layer_1, keep_prob)
# Output fully connected layer with a neuron for each class
out_layer = tf.matmul(layer_1_drop, weights['out']) + biases['out']
return out_layer,layer_1
logits,hidden_layer = neural_net(x)
prediction = tf.nn.softmax(logits)
loss_op = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=logits, labels=y_true))
optimizer = tf.train.AdamOptimizer(learning_rate=learning_rate)
train_op = optimizer.minimize(loss_op)
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(y_true, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
init = tf.global_variables_initializer()
saver = tf.train.Saver()
with tf.Session() as sess:
# Run the initializer
sess.run(init)
if _SAVEFLAG != 1 and _TRAINING != 1:
saver_restore = tf.train.import_meta_graph(_LOADDIR+'DAEandDNN_save.meta')
saver_restore.restore(sess, tf.train.latest_checkpoint(_LOADDIR))
if _TRAINING == 1:
for step in range(1, num_steps+1):
batch_x, batch_y = data_set.next_batch(batch_size)
batch_y = tf.one_hot(batch_y,3).eval()
# print(batch_x.shape,batch_y.shape)
# Run optimization op (backprop)
sess.run(train_op, feed_dict={x: batch_x, y_true: batch_y,keep_prob: 0.3})
if step % display_step == 0 or step == 1:
# Calculate batch loss and accuracy
loss, acc = sess.run([loss_op, accuracy], feed_dict={x: batch_x,y_true: batch_y,keep_prob: 1})
print("Step " + str(step) + ", Minibatch Loss= " + "{:.4f}".format(loss) + ", Training Accuracy= " + "{:.3f}".format(acc))
print("Optimization Finished!")
if _SAVEFLAG == 1:
saver.save(sess, _LOADDIR+'DAEandDNN_save')
print("Testing Accuracy:", sess.run(accuracy, feed_dict={x: data_set.data_set,y_true: tf.one_hot(data_set.label_set, 3).eval(),keep_prob: 1}))
output,hidden = sess.run([logits,hidden_layer], feed_dict={x: data_set.data_set,y_true: tf.one_hot(data_set.label_set, 3).eval(),keep_prob: 1})
# do_PCA(output, data_set.label_set, label='After training\nOutput Layer PCA Analysis')
# Z, label_x, label_y, label_z=do_PCA(hidden, data_set.label_set, label='After training\nHidden Layer PCA Analysis')
# np.random.seed(42)
# k_cls = 3
# cluster = 'kmeans'
# if cluster == 'kmeans':
# # kmeans = KMeans(n_clusters=k_cls,init='k-means++',n_init=k_cls)
# kmeans = KMeans(n_clusters=k_cls)
# kmeans.fit(Z)
# labels = kmeans.labels_
# elif cluster == 'dbscan':
# db = DBSCAN(eps=1, min_samples=10).fit(Z)
# labels = db.labels_
#
# plot_scatter(Z, labels, 3, 'kmeans', label_x, label_y, label_z)
print(hidden.shape,output.shape)
plt.show()