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"""
Module allows users to run several CNN architectures in a loop to compare multiple models in a tf loop
"""
from AuxiliaryCNN import csv_generator, text_to_labels, get_batch
import numpy as np
import tensorflow as tf
import time
# constants
DIR_PATH = "/data/scratch/epeake/Google-Doodles/"
MODEL_PATH = "./qd_model/"
BATCH_SIZE = 40
HEIGHT = 256
WIDTH = 256
N_EPOCHS = 4
START_TIME = time.time()
label_to_index = text_to_labels(DIR_PATH)
class_eye = np.eye(len(label_to_index))
n_outputs = len(label_to_index)
def cnn_model(model_type, l_r):
learning_rate = l_r
with tf.device("/gpu:1"):
if model_type == "VGG":
X = tf.placeholder("float", [None, HEIGHT, WIDTH, 1], name="X")
Y = tf.placeholder("float", [None, n_outputs], name="Y")
conv_1_1 = tf.layers.conv2d(X, filters=64, kernel_size=3,
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_1_1")
conv_1_2 = tf.layers.conv2d(conv_1_1, filters=64, kernel_size=3,
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_1_2")
pool1 = tf.nn.max_pool(conv_1_2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding='SAME', name="pool1")
conv_2_1 = tf.layers.conv2d(pool1, filters=128, kernel_size=3,
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_2_1")
conv_2_2 = tf.layers.conv2d(conv_2_1, filters=128, kernel_size=3,
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_2_2")
pool2 = tf.nn.max_pool(conv_2_2, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name="pool2")
conv_3_1 = tf.layers.conv2d(pool2, filters=256, kernel_size=3,
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_3_1")
conv_3_2 = tf.layers.conv2d(conv_3_1, filters=256, kernel_size=3,
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_3_2")
conv_3_3 = tf.layers.conv2d(conv_3_2, filters=256, kernel_size=3,
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_3_3")
pool3 = tf.nn.max_pool(conv_3_3, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name="pool3")
conv_4_1 = tf.layers.conv2d(pool3, filters=512, kernel_size=3,
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_4_1")
conv_4_2 = tf.layers.conv2d(conv_4_1, filters=512, kernel_size=3,
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_4_2")
conv_4_3 = tf.layers.conv2d(conv_4_2, filters=512, kernel_size=3,
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_4_3")
pool4 = tf.nn.max_pool(conv_4_3, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name="pool4")
conv_5_1 = tf.layers.conv2d(pool4, filters=512, kernel_size=3,
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_5_1")
conv_5_2 = tf.layers.conv2d(conv_5_1, filters=512, kernel_size=3,
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_5_2")
conv_5_3 = tf.layers.conv2d(conv_5_2, filters=512, kernel_size=3,
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_5_3")
pool5 = tf.nn.max_pool(conv_5_3, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name="pool5")
pre_fully_connected = tf.contrib.layers.flatten(pool5, scope="flattened")
fully_connected_1 = tf.layers.dense(pre_fully_connected, 410,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.nn.relu, name="fc1")
fully_connected_2 = tf.layers.dense(fully_connected_1, 410,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.nn.relu, name="fc2")
logits = tf.layers.dense(fully_connected_2, n_outputs, activation=tf.nn.relu, name="logits")
elif model_type == "Alex":
X = tf.placeholder("float", [None, HEIGHT, WIDTH, 1], name="X")
Y = tf.placeholder("float", [None, n_outputs], name="Y")
conv_1_1 = tf.layers.conv2d(X, filters=96, kernel_size=11, strides=(4, 4),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="VALID",
activation=tf.nn.relu, name="conv_1_1")
pool1 = tf.nn.max_pool(conv_1_1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name="pool1")
conv_2_1 = tf.layers.conv2d(pool1, filters=256, kernel_size=5, strides=(1, 1),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="VALID",
activation=tf.nn.relu, name="conv_2_1")
pool2 = tf.nn.max_pool(conv_2_1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name="pool2")
conv_3_1 = tf.layers.conv2d(pool2, filters=348, kernel_size=3, strides=(1, 1),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_3_1")
conv_3_2 = tf.layers.conv2d(conv_3_1, filters=348, kernel_size=3, strides=(1, 1),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_3_2")
conv_3_3 = tf.layers.conv2d(conv_3_2, filters=256, kernel_size=3, strides=(1, 1),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_3_3")
pool3 = tf.nn.max_pool(conv_3_3, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name="pool3")
pre_fully_connected = tf.contrib.layers.flatten(pool3, scope="flattened")
fully_connected_1 = tf.layers.dense(pre_fully_connected, 410,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.nn.relu, name="fc1")
fully_connected_2 = tf.layers.dense(fully_connected_1, 410,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.nn.relu, name="fc2")
logits = tf.layers.dense(fully_connected_2, n_outputs, activation=tf.nn.relu, name="logits")
elif model_type == "AlexDeep":
X = tf.placeholder("float", [None, HEIGHT, WIDTH, 1], name="X")
Y = tf.placeholder("float", [None, n_outputs], name="Y")
conv_1_1 = tf.layers.conv2d(X, filters=96, kernel_size=11, strides=(4, 4),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="VALID",
activation=tf.nn.relu, name="conv_1_1")
pool1 = tf.nn.max_pool(conv_1_1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name="pool1")
conv_2_1 = tf.layers.conv2d(pool1, filters=256, kernel_size=5, strides=(1, 1),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="VALID",
activation=tf.nn.relu, name="conv_2_1")
pool2 = tf.nn.max_pool(conv_2_1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name="pool2")
conv_3_1 = tf.layers.conv2d(pool2, filters=348, kernel_size=3, strides=(1, 1),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_3_1")
conv_3_2 = tf.layers.conv2d(conv_3_1, filters=348, kernel_size=3, strides=(1, 1),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_3_2")
conv_3_3 = tf.layers.conv2d(conv_3_2, filters=256, kernel_size=3, strides=(1, 1),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_3_3")
pool3 = tf.nn.max_pool(conv_3_3, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name="pool3")
pre_fully_connected = tf.contrib.layers.flatten(pool3, scope="flattened")
fully_connected_1 = tf.layers.dense(pre_fully_connected, 4010,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.nn.relu, name="fc1")
fully_connected_2 = tf.layers.dense(fully_connected_1, 4010,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.nn.relu, name="fc2")
logits = tf.layers.dense(fully_connected_2, n_outputs, activation=tf.nn.relu, name="logits")
elif model_type == "AlexDeep2":
X = tf.placeholder("float", [None, HEIGHT, WIDTH, 1], name="X")
Y = tf.placeholder("float", [None, n_outputs], name="Y")
conv_1_1 = tf.layers.conv2d(X, filters=96, kernel_size=11, strides=(4, 4),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="VALID",
activation=tf.nn.relu, name="conv_1_1")
pool1 = tf.nn.max_pool(conv_1_1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name="pool1")
conv_2_1 = tf.layers.conv2d(pool1, filters=256, kernel_size=5, strides=(1, 1),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="VALID",
activation=tf.nn.relu, name="conv_2_1")
pool2 = tf.nn.max_pool(conv_2_1, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name="pool2")
conv_3_1 = tf.layers.conv2d(pool2, filters=348, kernel_size=3, strides=(1, 1),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_3_1")
conv_3_2 = tf.layers.conv2d(conv_3_1, filters=348, kernel_size=3, strides=(1, 1),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_3_2")
conv_3_3 = tf.layers.conv2d(conv_3_2, filters=256, kernel_size=3, strides=(1, 1),
kernel_initializer=tf.contrib.layers.xavier_initializer(), padding="SAME",
activation=tf.nn.relu, name="conv_3_3")
pool3 = tf.nn.max_pool(conv_3_3, ksize=[1, 3, 3, 1], strides=[1, 2, 2, 1], padding='SAME', name="pool3")
pre_fully_connected = tf.contrib.layers.flatten(pool3, scope="flattened")
fully_connected_1 = tf.layers.dense(pre_fully_connected, 4010,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.nn.relu, name="fc1")
fully_connected_2 = tf.layers.dense(fully_connected_1, 410,
kernel_initializer=tf.contrib.layers.xavier_initializer(),
activation=tf.nn.relu, name="fc2")
logits = tf.layers.dense(fully_connected_2, n_outputs, activation=tf.nn.relu, name="logits")
else:
raise ValueError("Unsupported model_type")
with tf.name_scope("xent"):
xent = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=logits, labels=Y, name="softmax"))
tf.summary.scalar("Cross Entropy", xent)
with tf.name_scope("train"):
train_step = tf.train.AdamOptimizer(learning_rate=learning_rate).minimize(xent)
with tf.name_scope("accuracy"):
correct_prediction = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))
accuracy = tf.reduce_mean(tf.cast(correct_prediction, "float"), name="accuracy_op")
tf.summary.scalar("Accuracy", accuracy)
with tf.name_scope("num_right"):
correct_prediction_2 = tf.equal(tf.argmax(logits, 1), tf.argmax(Y, 1))
correct = tf.reduce_sum(tf.cast(correct_prediction_2, "float"), name="correct")
var_summary = tf.summary.merge_all()
config = tf.ConfigProto()
config.allow_soft_placement = True
with tf.Session(config=config) as sess:
sess.run(tf.global_variables_initializer())
saver = tf.train.Saver()
writer = tf.summary.FileWriter(MODEL_PATH + "tboard", filename_suffix="lr-" + str(l_r) + "-mt-" + model_type)
writer.add_graph(sess.graph)
total_batch_number = 1
for epoch in range(N_EPOCHS):
csv_gen = csv_generator(DIR_PATH, BATCH_SIZE)
while True:
try:
X_batch, Y_batch = get_batch(csv_gen, label_to_index, class_eye)
except StopIteration:
break
sess.run(train_step, feed_dict={X: X_batch, Y: Y_batch})
if total_batch_number % 100 == 0:
[train_accuracy, summ] = sess.run([accuracy, var_summary], feed_dict={X: X_batch, Y: Y_batch})
writer.add_summary(summ, total_batch_number)
print("Epoch:", epoch + 1, "Total Batch Number:", total_batch_number, "Train accuracy:", train_accuracy)
if total_batch_number % 2500 == 0:
saver.save(sess, MODEL_PATH + "lr-" + str(l_r) + "-mt-" + model_type + "/cnnmodel", total_batch_number)
total_batch_number += 1
print("Epoch time:", (time.time() - START_TIME) // 3600, "hr", ((time.time() - START_TIME) % 3600) / 60, "min")
# final reports
summ = sess.run(var_summary, feed_dict={X: X_batch, Y: Y_batch})
writer.add_summary(summ, total_batch_number)
saver.save(sess, MODEL_PATH + "lr-" + str(l_r) + "-mt-" + model_type + "/cnnmodel", total_batch_number)
# check CV accuracy
print("\n\n\n" + model_type + " CV Accuracy \n\n\n\n")
csv_gen = csv_generator(DIR_PATH, 1, file_name="cross_validate.csv", shuffle=False)
test_correct = 0
batch_number = 1
while True:
try:
X_batch, Y_batch = get_batch(csv_gen, label_to_index, class_eye)
test_correct += sess.run(correct, feed_dict={X: X_batch, Y: Y_batch})
if batch_number % 1000 == 0:
print("Batch number:", batch_number, "CV batch accuracy:", test_correct / batch_number)
except StopIteration:
break
except IndexError:
break
batch_number += 1
print(model_type, "CV Accuracy =", test_correct / batch_number)
if model_type == "AlexDeep2":
print("\n\n\n AlexDeep2 CV train \n\n\n\n")
for epoch in range(N_EPOCHS):
csv_gen = csv_generator(DIR_PATH, BATCH_SIZE, file_name="cross_validate.csv")
while True:
try:
X_batch, Y_batch = get_batch(csv_gen, label_to_index, class_eye)
except StopIteration:
break
sess.run(train_step, feed_dict={X: X_batch, Y: Y_batch})
if total_batch_number % 100 == 0:
[train_accuracy, summ] = sess.run([accuracy, var_summary], feed_dict={X: X_batch, Y: Y_batch})
writer.add_summary(summ, total_batch_number)
print("Epoch CV:", epoch + 1, "Total Batch Number:", total_batch_number, "Train accuracy:",
train_accuracy)
total_batch_number += 1
print("Epoch cv time:", (time.time() - START_TIME) // 3600, "hr", ((time.time() - START_TIME) % 3600) / 60,
"min")
# check test accuracy
print("\n\n\n Test Accuracy \n\n\n\n")
csv_gen = csv_generator(DIR_PATH, 1, file_name="test.csv", shuffle=False)
test_correct = 0
batch_number = 1
while True:
try:
X_batch, Y_batch = get_batch(csv_gen, label_to_index, class_eye)
test_correct += sess.run(correct, feed_dict={X: X_batch, Y: Y_batch})
if batch_number % 1000 == 0:
print("batch number:", batch_number, "test accuracy:", test_correct / batch_number)
except StopIteration:
break
except IndexError:
break
batch_number += 1
print(model_type, "Test Accuracy =", test_correct / batch_number)
def main():
for l_r in [0.00009]:
for m_type in ["AlexDeep2", "AlexDeep"]:
tf.reset_default_graph()
print("Starting", m_type, "with learning rate", str(l_r))
cnn_model(m_type, l_r)
if __name__ == "__main__":
main()