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catnocat_cnn.py
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catnocat_cnn.py
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# coding: utf-8
import argparse
import numpy as np
import tensorflow as tf
from tqdm import tqdm
import time
from sklearn.model_selection import train_test_split
import os
from flask import Flask, request, render_template
import matplotlib.pyplot as plt
import scipy.misc
from PIL import Image
app = Flask(__name__)
tf.logging.set_verbosity(tf.logging.INFO)
parser = argparse.ArgumentParser(description='Cat or no cat?')
parser.add_argument('-s', required=False, help='Serve http', action='store_true')
serve = parser.parse_args().s
square_size = 200
img_path = "../../datasets/imagenet/parsed_cats_200/" # Dir with training data. File name starts from 1 or 0 determine class
n_epochs = 25
model_fpath = "../../models/kotniekot-cnn.ckpt" # Saved model path
model_name = "cnn-{}".format(int(time.time()))
n_inputs = square_size ** 2
filenames = os.listdir(img_path)
filenames_0 = [f for f in filenames if f.startswith("0")][:5000] # To balance dataset
filenames_1 = [f for f in filenames if f.startswith("1")]
filenames = filenames_0 + filenames_1
length = len(filenames)
labels = [int(img[0]) for img in filenames]
X_train, X_test, y_train, y_test = train_test_split(filenames, labels, test_size=0.25, random_state=42)
def parse_image(image_string):
image_decoded = tf.image.decode_image(image_string, channels=1)
image_decoded = tf.image.resize_image_with_crop_or_pad(image_decoded, square_size, square_size)
return image_decoded
def _load_image(filename):
img_string = tf.read_file(filename)
img = parse_image(img_string)
return img
def _get_image(filename, label):
image_string = tf.read_file(img_path + filename)
image_decoded = parse_image(image_string)
return image_decoded, label
def get_dataset(data, labels):
dataset = tf.data.Dataset.from_tensor_slices((data, labels))
dataset = dataset.map(_get_image)
dataset = dataset.batch(200)
dataset = dataset.repeat()
dataset = dataset.make_one_shot_iterator()
return dataset.get_next()
with tf.name_scope("dataset"):
X_train = tf.convert_to_tensor(X_train)
y_train = tf.convert_to_tensor(y_train)
X_test = tf.convert_to_tensor(X_test)
y_test = tf.convert_to_tensor(y_test)
train_iter = get_dataset(X_train, y_train)
test_iter = get_dataset(X_test, y_test)
X = tf.placeholder(tf.float32, shape=(None, 200, 200, 1), name="X")
y = tf.placeholder(tf.int64, shape=(None), name="y")
with tf.name_scope("conv_definition"):
f = tf.get_variable('conv1-fil', [5,5,1,15])
conv1 = tf.nn.conv2d(X, filter=f, strides=[1, 1, 1, 1], padding="SAME")
pool1 = tf.nn.max_pool(conv1, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")
f2 = tf.get_variable('conv2-fil', [3,3,15,15])
conv2 = tf.nn.conv2d(pool1, filter=f2, strides=[1, 1, 1, 1], padding="SAME")
pool2 = tf.nn.max_pool(conv2, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="VALID")
fc1 = tf.contrib.layers.flatten(pool2)
with tf.name_scope("conn_net_definition"):
hidden1 = tf.layers.dense(fc1, 7785, name="hidden1",
activation=tf.nn.relu)
hidden2 = tf.layers.dense(hidden1, 2000, name="hidden2",
activation=tf.nn.relu)
hidden3 = tf.layers.dense(hidden2, 1000, name="hidden3",
activation=tf.nn.relu)
hidden4 = tf.layers.dense(hidden3, 1000, name="hidden4",
activation=tf.nn.relu)
hidden5 = tf.layers.dense(hidden4, 700, name="hidden5",
activation=tf.nn.relu)
hidden6 = tf.layers.dense(hidden5, 500, name="hidden6",
activation=tf.nn.relu)
logits = tf.layers.dense(hidden6, 2, name="outputs")
with tf.name_scope("loss"):
xentropy = tf.nn.sparse_softmax_cross_entropy_with_logits(labels=y,
logits=logits)
loss = tf.reduce_mean(xentropy, name="loss")
learning_rate = 0.0001
with tf.name_scope("train"):
optimizer = tf.train.GradientDescentOptimizer(learning_rate)
training_op = optimizer.minimize(loss)
tf.summary.scalar('loss', loss)
with tf.name_scope("eval"):
cls = tf.nn.top_k(logits)
precision = tf.metrics.precision(cls[1], y)
tf.summary.scalar('precision', precision[1])
accuracy = tf.metrics.accuracy(cls[1], y)
tf.summary.scalar('accuracy', accuracy[1])
recall = tf.metrics.recall(cls[1], y)
tf.summary.scalar('recall', recall[1])
def train(sess, train_writer, test_writer):
init_time = time.time()
for j in range(n_epochs):
for i in tqdm(range(int(length * 0.75 / 200))):
input_data = sess.run(train_iter)
summary_train, _ = sess.run([merged, training_op], feed_dict={X: input_data[0], y: input_data[1]})
train_writer.add_summary(summary_train, i)
test_data = sess.run(test_iter)
summary_test, acc = sess.run([merged, accuracy], feed_dict={X: test_data[0], y: test_data[1]})
print("epoch: {} elapsed: {} acc {}".format(j, (time.time() - init_time), acc[0]))
test_writer.add_summary(summary_test, j)
sess = tf.Session()
merged = tf.summary.merge_all()
init = tf.global_variables_initializer()
merged = tf.summary.merge_all()
train_writer = tf.summary.FileWriter('/logs/{}/train'.format(model_name), sess.graph)
test_writer = tf.summary.FileWriter('/logs/{}/test'.format(model_name), sess.graph)
sess.run(init)
sess.run(tf.local_variables_initializer())
saver = tf.train.Saver()
total_parameters = 0
for variable in tf.trainable_variables():
# shape is an array of tf.Dimension
shape = variable.get_shape()
variable_parameters = 1
for dim in shape:
variable_parameters *= dim.value
total_parameters += variable_parameters
print(total_parameters)
if os.path.isfile(model_fpath):
saved = saver.restore(sess, model_fpath)
@app.route("/", methods=["GET", "POST"])
def cat_detect():
if request.method == 'POST':
file_string = request.files['fileToUpload'].read()
image = parse_image(file_string)
image = sess.run([image])
cnc = sess.run([cls], feed_dict={X: image})
if cnc[0][1][0][0]:
return "Cat!"
else:
return "Not cat:("
return render_template('upload_form.html.j2')
if serve:
app.run(host="0.0.0.0")
else:
train(sess, train_writer, test_writer)
saved = saver.save(sess, model_fpath)
#input_data = sess.run(train_iter)
#out = sess.run(fc1, feed_dict={X: input_data[0], y: input_data[1]})
#print(out[0].shape)
#from IPython import embed; embed()