model = nb.attach_conv_layer(model, 64, summary=True)
model = nb.attach_relu_layer(model)
model = nb.attach_conv_layer(model, 64, summary=True)
model = nb.attach_relu_layer(model)
model = nb.attach_pooling_layer(model)
model = nb.attach_cosh_layer(model)
model = nb.attach_conv_layer(model, 128, summary=True)
model = nb.attach_relu_layer(model)
model = nb.attach_conv_layer(model, 128, summary=True)
model = nb.attach_relu_layer(model)
model = nb.attach_pooling_layer(model)
model = nb.flatten(model)
model = nb.attach_dense_layer(model, 200, summary=True)
model = nb.attach_sigmoid_layer(model)
model = nb.attach_dense_layer(model, 32, summary=True)
model = nb.attach_sigmoid_layer(model)
model = nb.attach_dense_layer(model, 2)
prediction = nb.attach_softmax_layer(model)
with tf.name_scope("Optimization") as scope:
global_step = tf.Variable(0, name='global_step', trainable=False)
cost = tf.nn.softmax_cross_entropy_with_logits_v2(labels=target_labels,
logits=model)
cost = tf.reduce_mean(cost)
tf.summary.scalar("cost", cost)
optimizer = tf.train.AdamOptimizer().minimize(cost,
global_step=global_step)
def train():
dg = DataSetGenerator("training_face_rec")
MAX_LABELS = len(dg.data_labels)
dg.save_labels()
with tf.name_scope("Input") as scope:
input_img = tf.placeholder(dtype='float', shape=[None, 128, 128, 3], name="input")
with tf.name_scope("Target") as scope:
target_labels = tf.placeholder(dtype='float', shape=[None, MAX_LABELS], name="Targets")
with tf.name_scope("Keep_prob_input") as scope:
keep_prob = tf.placeholder(dtype='float',name='keep_prob')
nb = NetworkBuilder()
with tf.name_scope("ModelV2") as scope:
model = input_img
model = nb.attach_conv_layer(model, 32, summary=True)
model = nb.attach_relu_layer(model)
model = nb.attach_conv_layer(model, 32, summary=True)
model = nb.attach_relu_layer(model)
model = nb.attach_pooling_layer(model)
model = nb.attach_conv_layer(model, 64, summary=True)
model = nb.attach_relu_layer(model)
model = nb.attach_conv_layer(model, 64, summary=True)
model = nb.attach_relu_layer(model)
model = nb.attach_pooling_layer(model)
model = nb.attach_conv_layer(model, 128, summary=True)
model = nb.attach_relu_layer(model)
model = nb.attach_conv_layer(model, 128, summary=True)
model = nb.attach_relu_layer(model)
model = nb.attach_pooling_layer(model)
model = nb.flatten(model)
model = nb.attach_dense_layer(model, 200, summary=True)
model = nb.attach_sigmoid_layer(model)
model = nb.attach_dense_layer(model, 32, summary=True)
model = nb.attach_sigmoid_layer(model)
model = nb.attach_dense_layer(model, MAX_LABELS)
prediction = nb.attach_softmax_layer(model)
with tf.name_scope("Optimization") as scope:
global_step = tf.Variable(0, name='global_step', trainable=False)
cost = tf.nn.softmax_cross_entropy_with_logits(logits=model, labels=target_labels)
cost = tf.reduce_mean(cost)
tf.summary.scalar("cost", cost)
optimizer = tf.train.AdamOptimizer().minimize(cost, global_step=global_step)
with tf.name_scope('accuracy') as scope:
correct_pred = tf.equal(tf.argmax(prediction, 1), tf.argmax(target_labels, 1))
accuracy = tf.reduce_mean(tf.cast(correct_pred, tf.float32))
tf.summary.scalar("accuracy", accuracy)
# dg = DataSetGenerator("../data/twins")
test_x , test_y = dg.load_test_images(image_size=(128,128))
epochs = 10
batchSize = 10
saver = tf.train.Saver()
model_save_path="./saved model v2/"
model_name='model'
is_Train = True
with tf.Session() as sess:
summaryMerged = tf.summary.merge_all()
filename = "./summary_log/run" + datetime.datetime.now().strftime("%Y-%m-%d--%H-%M-%s")
# setting global steps
tf.global_variables_initializer().run()
if os.path.exists(model_save_path+'checkpoint'):
# saver = tf.train.import_meta_graph('./saved '+modelName+'/model.ckpt.meta')
saver.restore(sess, tf.train.latest_checkpoint(model_save_path))
if is_Train:
writer = tf.summary.FileWriter(filename+"_train", sess.graph)
test_writer = tf.summary.FileWriter(filename+"_test", sess.graph)
start_time = datetime.datetime.now()
for epoch in range(epochs):
batches = dg.get_mini_batches(batchSize,(128,128), allchannel=True)
for imgs ,labels in batches:
# X_train, X_test, y_train, y_test = train_test_split(imgs, labels, test_size=0.33, random_state=42)
imgs=np.divide(imgs, 255)
error, sumOut, acu, steps,_ = sess.run([cost, summaryMerged, accuracy,global_step,optimizer],
feed_dict={input_img: imgs, target_labels: labels})
writer.add_summary(sumOut, steps)
print("epoch=", epoch,"steps=",steps, "Total Samples Trained=", steps*batchSize, "err=", error, "accuracy=", acu)
if steps % 10 == 0: # Record summaries and test-set accuracy
error, sumOut, acu, steps = sess.run([cost,summaryMerged, accuracy,global_step], feed_dict={input_img: test_x, target_labels: test_y})
test_writer.add_summary(sumOut, steps)
print("Test : error= ", error, "epoch=", epoch,"steps=",steps, "accuracy=", acu)
if steps % 100 == 0:
print("Saving the model")
saver.save(sess, model_save_path+model_name, global_step=steps)
saver.save(sess, model_save_path+model_name)
sess.close()
end_time = datetime.datetime.now()
print(diff_time(start_time,end_time))