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assingment1.py
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assingment1.py
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# -*- coding: utf-8 -*-
"""assingment1.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/19P9jPCzu8lYHXlQHfBzaITe5HvlS8vob
# Assingment 1
> Name: Milind Shaileshkumar Parvatia
> Sid: s3806853
## **Importing Libraries**
Importing All libraries for this project
"""
import os
import pathlib
import shutil
import tempfile
from os import walk
import numpy as np
import pandas as pd
import tensorflow as tf
from IPython.display import display, HTML
from matplotlib import pyplot as plt
import matplotlib.image as mpimg
from sklearn.utils import resample
from sklearn.model_selection import train_test_split
from sklearn.model_selection import StratifiedKFold
from tensorflow.keras.models import Sequential, load_model, Model
from tensorflow.keras.layers import Input, BatchNormalization
from tensorflow.keras.layers import Dense, LSTM, GlobalAveragePooling1D, GlobalAveragePooling2D
from tensorflow.keras.layers import Activation, Flatten, Dropout
from tensorflow.keras.layers import Conv2D, MaxPooling2D, GlobalMaxPooling2D
from tensorflow.keras.layers import PReLU, LeakyReLU
pd.set_option('display.max_columns', None)
AUTOTUNE=tf.data.experimental.AUTOTUNE
"""### Retriving files from Github Repository"""
# Commented out IPython magic to ensure Python compatibility.
# Clone the entire repo.
!git clone -l -s git://github.com/milindparvatia/DL-Assingment-1.git
# %cd DL-Assingment-1
!ls
"""## **Tensorboard**
Tensorboard is used to present all the output matrix created using tensorflow in this notebook.
"""
os.mkdir(os.getcwd()+'/tensorboard_logs')
# Commented out IPython magic to ensure Python compatibility.
logdir = pathlib.Path("/tensorboard_logs")
#pathlib.Path(tempfile.mkdtemp())/"tensorboard_logs"
shutil.rmtree(logdir, ignore_errors=True)
# Load the TensorBoard notebook extension
# %load_ext tensorboard
# Open an embedded TensorBoard viewer
# %tensorboard --logdir {logdir}/models
"""## **Dataset**
Head Pose Image Database
The data for this assignment is a modified version of the data available at: [Head Pose Image Database](http://www-prima.inrialpes.fr/perso/Gourier/Faces/HPDatabase.html)
**Licence agreement: The dataset can only be used for the purpose of this assignment. Sharing or distributing this data or using this data for any other commercial or non-commercial purposes is prohibited.**
The head pose database is a benchmark of 2790 monocular face images of 15 persons with variations of pan and tilt angles from -90 to +90 degrees. For every person, 2 series of 93 images (93 different poses) are available. The purpose of having 2 series per person is to be able to train and test algorithms on known and unknown faces (cf. sections 2 and 3). People in the database wear glasses or not and have various skin colour. Background is willingly neutral and uncluttered in order to focus on face operations.
The image dimensions are [192,144]
**Tilt (Vertical angle)** = {-90, -60, -30, -15, 0, +15, +30, +60, +90},
Negative values - bottom, Positive values - top;
**Pan (Horizontal angle)** = {-90, -75, -60, -45, -30, -15, 0, +15, +30, +45, +60, +75, +90},
Negative values - left, Positive values - right;
### 1) Training dataset
"""
train_df = pd.read_csv('train_data.csv')
train_df.head()
#printing shape of dataset
print("Shape of training dataset:",train_df.shape)
"""### 2) Testing Dataset"""
test_df = pd.read_csv('test_data.csv')
test_df.head()
#printing shape of dataset
print("Shape of testing dataset:",test_df.shape)
"""### 3) Accessing Images
All the images are in directory modified_data, to access Images I have created common method. It reads all the files in modified_data_, and copy them into new folder 'faces', it rename all images by removing 'face_' from it and only allows image numbers as file name.(I took this steps because tensorflow only accepts integers as filepath for creating tensors.)
I also replace both training and testing dataset from 'face_Int.jpg' to 'Int.jpg'.
"""
# used following cmd to get path for current folder
curr_dir = os.getcwd()
image_names = []
if os.path.exists(curr_dir+"/faces"):
images_dir = curr_dir+"/faces"
for (dirpath, dirnames, filenames) in walk(images_dir):
image_names.extend(filenames)
break
for i, name in enumerate(image_names):
train_df['filename'] = train_df['filename'].replace('face_'+str(name), name)
test_df['filename'] = test_df['filename'].replace('face_'+str(name), name)
else:
os.mkdir('faces')
images_dir = pathlib.Path(curr_dir+"/modified_data")
for (dirpath, dirnames, filenames) in walk(images_dir):
image_names.extend(filenames)
break
for i, name in enumerate(image_names):
new_name = "_".join(name.split('_')[1:2])
old_path = str(images_dir)+"/"+name
new_path = os.path.join(curr_dir+"/faces/", new_name)
os.rename(old_path, new_path)
train_df['filename'] = train_df['filename'].replace(str(name), new_name)
test_df['filename'] = test_df['filename'].replace(str(name), new_name)
images_dir = curr_dir+"/faces/"
train_image_count = len(list(pathlib.Path(images_dir).glob('*.jpg')))
print("Total numbers of Images we have to process are",train_image_count)
## I have convert filenames to int to make it easier to fill into tf.data
print("New filename in test_df dataset")
test_df.head()
"""Let's look at our image dataset with tilt and pan values."""
plt.figure(figsize=(10,10))
for i in range(16):
plt.subplot(4,4,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
image = mpimg.imread(str(images_dir)+'/'+train_df['filename'].iloc[i]) # images are color images
plt.imshow(image)
plt.title(train_df['filename'].iloc[i])
plt.xlabel([ str(train_df['tilt'].iloc[i])+' || '+str(train_df['pan'].iloc[i]) ])
plt.show()
"""## **Data Preparation**
### 1) Cleaning datasets
"""
train_df.isnull().sum()
test_df.isnull().sum()
"""From the above measurements we can conclude that the Given datasets have zero null values and are in perfect condition for further processing.
### 2) Checking Frequencies of Labels
Showing Label Distribution so we can get better idea what type of data we are dealing.
"""
fig, ax = plt.subplots()
train_df['tilt'].value_counts().plot(ax=ax, kind='bar')
train_df['tilt'].value_counts()
fig, ax = plt.subplots()
train_df['pan'].value_counts().plot(ax=ax, kind='bar')
train_df['pan'].value_counts()
"""Here we can see that our target features are imbalanced, but for this project [I am ignoring imbalnced data, since I'm going to use ADAM optimizer.](https://stats.stackexchange.com/questions/283170/when-is-unbalanced-data-really-a-problem-in-machine-learning)
## One Hot Encoding of target value
For encoding I have used One hot Encoder from sklearn library which convert only classes to 1 if present otherwise convert them to 0.
"""
from sklearn.preprocessing import OneHotEncoder
def ohe(x):
return OneHotEncoder(categories='auto').fit(x.reshape(-1,1)).transform(x.reshape(-1,1)).toarray().astype('int64')
"""### 1) OneHotEncoding of Tilt
Tilt column have 9 diffrent values which are {-90, -60, -30, -15, 0, +15, +30, +60, +90} so when we performs OneHotEncoding it creates 9 columns with every column representing one label of tilt angle.
"""
y_tilt = pd.DataFrame(ohe(np.array(train_df.tilt)))
y_tilt.shape
"""### 2) OneHotEncoding of Pan
Pan have 13 diffrent values {-90, -75, -60, -45, -30, -15, 0, +15, +30, +45, +60, +75, +90} so it will creates 13 diffrent columns.
"""
y_pan = pd.DataFrame(ohe(np.array(train_df.pan)))
y_pan.shape
"""### 3) Creating Target values
for this problem I have merged two target values into single numpy array so that we can pass it to train_test_split method of sklearn.
"""
targets = np.concatenate((y_tilt, y_pan), axis=1)
targets[:3]
print("Shape of target numpy array:",targets.shape)
"""## **Training-Validation data split from train_df dataset**
Here I have used train_test_split from sklearn with startify to shuffle dataset before spliting them into training and validation dataset for model.
I have used test_size = 0.2 which will splits 80% for training data and 20% for validating data.
"""
def train_test_splits(X,y):
x_train, x_val, y_train, y_val = train_test_split(X, y, test_size=.2, random_state=292, shuffle=True, stratify=y)
return x_train, x_val, y_train, y_val
X_train, X_val, y_train, y_val = train_test_splits(train_df['filename'], targets)
X_test = test_df.filename
"""Adding image's file path as prefix to image names so that it can be retrived."""
X_train = [os.path.join(images_dir, str(f)) for f in X_train]
X_val = [os.path.join(images_dir, str(f)) for f in X_val]
y_train = np.array(y_train)
y_val = np.array(y_val)
print("***************************")
print("Splits of Labels")
print("***************************")
print("Size of training: ", len(X_train))
print("Size of validation: ", len(X_val))
print("\n***************************")
print("Input Values:")
print("***************************")
print(X_train[:3])
print("\n***************************")
print("Target Values:")
print("***************************")
print(y_train[:3])
"""## **Data pipeline**
To fully use gpu for training I have used tensorflow's Distributed Mirrored Strategy which make it easier for training models most efficient way on gpu.
"""
strategy = tf.distribute.MirroredStrategy()
print('Number of devices: {}'.format(strategy.num_replicas_in_sync))
"""I have created data pipeline for easier approch for creating tensors, in one go, Using this mehtods, I have created tensor of <(training data),(target(tilt),(target(pan))>.
Since our number of training images are very less for deep learning problem, I have used Ausgmentation of images to create variasions of imaeges for more complexity. I have used following augmentation,
1. random_brightness
2. random_saturation
3. random_hue
"""
BUFFER_SIZE = 128
IMG_HEIGHT = 144
IMG_WIDTH = 192
BATCH_SIZE_PER_REPLICA = 256
BATCH_SIZE = BATCH_SIZE_PER_REPLICA * strategy.num_replicas_in_sync
# pipeline for training and validation datasets
def data_pipeline(X, y, augmenting=False):
filenames = tf.convert_to_tensor(X, dtype=tf.string)
label1 = tf.convert_to_tensor(y[:, :9])
label2 = tf.convert_to_tensor(y[:, 9:22])
dataset = tf.data.Dataset.from_tensor_slices((filenames, (label1, label2)))
def map_fn(path, label):
# path/label represent values for a single example
image = tf.image.decode_jpeg(tf.io.read_file(path), channels=3)
if augmenting:
image = tf.image.random_brightness(image, 0.2)
image = tf.image.random_saturation(image, 0, 4)
image = tf.image.random_hue(image, 0.3)
image = tf.cast(image, dtype=tf.float32) / 255.0
image = tf.reshape(image, [144, 192, 3])
image = tf.image.resize_with_crop_or_pad(image, 192, 192)
return image, label
# num_parallel_calls > 1 induces intra-batch shuffling
dataset = dataset.map(map_fn, num_parallel_calls=AUTOTUNE)
dataset = dataset.prefetch(buffer_size=AUTOTUNE)
return dataset
# pipeline for testing datasets
def creating_test_pipeline(X):
filenames = tf.convert_to_tensor(X, dtype=tf.string)
dataset = tf.data.Dataset.from_tensor_slices((filenames))
def map_test_fn(path):
# path/label represent values for a single example
image = tf.image.decode_jpeg(tf.io.read_file(path), channels=3)
image = tf.cast(image, dtype=tf.float32) / 255.0
image = tf.reshape(image, [144, 192, 3])
image = tf.image.resize_with_crop_or_pad(image, 192, 192)
return image
dataset = dataset.map(map_test_fn, num_parallel_calls=AUTOTUNE)
dataset = dataset.prefetch(buffer_size=AUTOTUNE)
return dataset
# Creating dataset for training on models with no augmentations
train_ds = data_pipeline(X_train,y_train).cache().repeat(BUFFER_SIZE).batch(BATCH_SIZE)
# Creating small dataset for training on model
train_ds_tiny = data_pipeline(
X_train[:BATCH_SIZE],
y_train[:BATCH_SIZE]
).cache().repeat(BUFFER_SIZE).batch(BATCH_SIZE)
# Creating dataset for training on models with augmentations
train_ds_aug = data_pipeline(X_train,y_train,True).cache().repeat(BUFFER_SIZE).batch(BATCH_SIZE)
# Creating full dataset for training on models with augmentations
full_train_ds_aug = data_pipeline(
np.concatenate([X_train,X_val]),
np.concatenate([y_train,y_val]),
True
).cache().repeat(BUFFER_SIZE).batch(BATCH_SIZE)
# Creating Validation dataset for validation
val_ds = data_pipeline(X_val,y_val).batch(BATCH_SIZE)
# Crating Testing dataset for testing models after training
test_ds = creating_test_pipeline(X_test).cache().repeat(BUFFER_SIZE).batch(BATCH_SIZE)
train_ds
"""let's see if image augmation works or not,"""
image, label = next(iter(train_ds_aug.batch(1)))
plt.figure(figsize=(10,10))
for i in range(16):
plt.subplot(4,4,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
arr_ = np.squeeze(image[0][i])
plt.imshow(arr_)
plt.show()
"""## Resnet Model
For predicting face's tilt and pan angle I have decided to use Resnet-34 which uses 34 layers Architecutre and it's one of the most common resnet. I have recreated the architecture following way.
---
![1_2ns4ota94je5gSVjrpFq3A.png](data:image/png;base64,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)
**Custom ResidualBlock for NN**, I have used following funtion to create custom ResidualBlock for NN which makes is more easier to experiment with hyperparameters and diffrent iterations of models.
"""
class ResidualBlock(tf.keras.layers.Layer):
# Initialize components of the model
def __init__(self, filter_num, stride=1, reg_lambda=0.0):
super(ResidualBlock, self).__init__()
self.conv1 = tf.keras.layers.Conv2D(filters=filter_num,
kernel_size=(3, 3),
strides=stride,
kernel_initializer="he_normal",
kernel_regularizer=tf.keras.regularizers.l2(reg_lambda),
padding="same")
self.bn1 = tf.keras.layers.BatchNormalization(momentum=0.7)
self.conv2 = tf.keras.layers.Conv2D(filters=filter_num,
kernel_size=(3, 3),
strides=1,
kernel_initializer="he_normal",
kernel_regularizer=tf.keras.regularizers.l2(reg_lambda),
padding="same")
self.bn2 = tf.keras.layers.BatchNormalization(momentum=0.7)
if stride != 1:
self.downsample = tf.keras.Sequential()
self.downsample.add(tf.keras.layers.Conv2D(filters=filter_num,
kernel_size=(1, 1),
kernel_initializer="he_normal",
kernel_regularizer=tf.keras.regularizers.l2(reg_lambda),
strides=stride))
self.downsample.add(tf.keras.layers.BatchNormalization(momentum=0.7))
else:
self.downsample = lambda x: x
# Define the forward function
def call(self, inputs, training=None, **kwargs):
residual = self.downsample(inputs)
x = self.conv1(inputs)
x = self.bn1(x, training=training)
x = tf.nn.relu(x)
x = self.conv2(x)
x = self.bn2(x, training=training)
output = tf.nn.relu(tf.keras.layers.add([residual, x]))
return output
def get_config(self):
config = super().get_config().copy()
config.update({
'conv1': self.conv1,
'bn1': self.bn1,
'conv2': self.conv2,
'bn2': self.bn2,
'downsample': self.downsample,
})
return config
"""This function is created to return resnet model with desiered NN layers."""
def get_resnet_model(filters, block_size, reg_lambda=0.0):
model_input = Input(shape=(192, 192, 3))
#initial segment
x = Conv2D(filters=64,
kernel_size=(7,7),
strides=1,
kernel_initializer="he_normal",
kernel_regularizer=tf.keras.regularizers.l2(reg_lambda),
padding="same"
)(model_input)
x = BatchNormalization(momentum=0.7)(x)
x = Activation('relu')(x)
x = MaxPooling2D((3,3))(x)
#Stack of residual blocks
for nFilters, nBlocks in zip(filters, block_size):
x = ResidualBlock(nFilters, stride=2, reg_lambda=reg_lambda)(x)
for _ in range(1, nBlocks):
x = ResidualBlock(nFilters, stride=1, reg_lambda=reg_lambda)(x)
# Final part
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Flatten()(x)
y1 = tf.keras.layers.Dense(9, activation=tf.nn.softmax, kernel_regularizer=tf.keras.regularizers.l2(reg_lambda), kernel_initializer="he_normal")(x)
y2 = tf.keras.layers.Dense(13, activation=tf.nn.softmax, kernel_regularizer=tf.keras.regularizers.l2(reg_lambda), kernel_initializer="he_normal")(x)
model = Model(inputs=model_input, outputs=[y1, y2])
return model
"""* For Optimizer I have used Adam with learning rate decaying over the numbers of steps per epochs.
* I have used custom callbacks with early rate stopping, which stops the training if loss function stops improving.
* I have used tensorboards in callbacks to ceate outputs of matrix in more readeable manner.
"""
checkpoint_dir = './training_checkpoints'
# # Name of the checkpoint files
checkpoint_prefix = os.path.join(checkpoint_dir, "ckpt_{epoch}")
def get_callbacks(name, early_stop=True):
if early_stop:
return [
tf.keras.callbacks.EarlyStopping(monitor='val_loss', patience=25),
tf.keras.callbacks.TensorBoard("{}/{}".format(
str(logdir), str(name)), histogram_freq=60, embeddings_freq=60),
# tf.keras.callbacks.LearningRateScheduler(decay),
]
else:
return [
tf.keras.callbacks.TensorBoard("{}/{}".format(str(logdir), str(name))),
]
"""I have used custom plotter funtion to plot the model output of matrixes after fitting in training for better comparision."""
from itertools import cycle
def plotter(history_hold, metric = 'binary_crossentropy', ylim=[0.0, 1.0]):
cycol = cycle('bgrcmk')
for name, item in history_hold.items():
y_train = item.history[metric]
y_val = item.history['val_' + metric]
x_train = np.arange(0,len(y_val))
c=next(cycol)
plt.plot(x_train, y_train, c+'-', label=name+'_train')
plt.plot(x_train, y_val, c+'--', label=name+'_val')
plt.legend()
plt.xlim([1, max(plt.xlim())])
plt.ylim(ylim)
plt.xlabel('Epoch')
plt.ylabel(metric)
plt.grid(True)
"""## Model Training"""
from tensorflow.keras.optimizers import Adam
STEPS_PER_EPOCH = len(X_train)//BATCH_SIZE
lr = 0.001
lr_schedule = tf.keras.optimizers.schedules.InverseTimeDecay(
lr,
decay_steps=STEPS_PER_EPOCH*1000,
decay_rate=1,
staircase=False
)
optimizer = Adam(learning_rate=lr_schedule)
epoch_size = 250
"""## Sanity Check
* Before working on main model, I have performed sanity check to see it the data processed is working on model or not.
* This way it makes easier for me to identify any error before actual model training which saves alot of time.
### 1) Tiny model
First I have tired training data on small model to check if it underfits or not.
"""
tiny_histories = {}
with strategy.scope():
resnet_tiny_model = get_resnet_model([64], [1])
resnet_tiny_model.compile(
optimizer=optimizer,
loss=[
tf.losses.CategoricalCrossentropy(),
tf.losses.CategoricalCrossentropy()
],
metrics= ['accuracy'])
tiny_histories['resnet_tiny_model'] = resnet_tiny_model.fit(
train_ds,
epochs = epoch_size,
validation_data = (val_ds),
steps_per_epoch = len(X_train)//BATCH_SIZE,
validation_steps = len(X_val)//BATCH_SIZE,
verbose=0,
callbacks=get_callbacks('models/resnet_tiny_model')
)
plotter(tiny_histories, ylim=[0.0, 20], metric = 'loss')
"""### 2) Tiny Dataset
After that, I have used tiny dataset with small model to check if it is overfiting or not.
"""
with strategy.scope():
tiny_dataset = get_resnet_model([64], [1])
tiny_dataset.compile(
optimizer=optimizer,
loss=[
tf.losses.CategoricalCrossentropy(),
tf.losses.CategoricalCrossentropy()
],
metrics= ['accuracy'])
tiny_histories['tiny_dataset'] = tiny_dataset.fit(
train_ds_tiny,
epochs = epoch_size,
validation_data = (val_ds),
steps_per_epoch = 1,
validation_steps = 1,
verbose=0,
callbacks=get_callbacks('models/tiny_dataset')
)
plotter(tiny_histories, ylim=[0.0, 20], metric = 'loss')
"""Our dataset passed both tests so now we can starts actual model training.
## Base Model
---
For model training I have created a base model, which will be used thourout following trainings of models.
"""
m_histories = {}
with strategy.scope():
base_model = get_resnet_model([64,128,256,512], [3,4,6,3])
base_model.compile(
optimizer=optimizer,
loss=[
tf.losses.CategoricalCrossentropy(),
tf.losses.CategoricalCrossentropy()
],
metrics= ['accuracy'])
m_histories['base_model'] = base_model.fit(
train_ds,
epochs = epoch_size,
validation_data = (val_ds),
steps_per_epoch = len(X_train)//BATCH_SIZE,
validation_steps = len(X_val)//BATCH_SIZE,
verbose=0,
callbacks=get_callbacks('models/base_model')
)
plotter(m_histories, ylim=[0.0, 10], metric = 'loss')
"""* Since the dataset for the given problem is very small, even though the validation curve for based model is decreasing with training curve, my based model has very high variance.
* To reduce high varience I have tried following methods,
1. Data Augmentation
2. Regularisation
3. Raise momentum in BatchNormalizer since model is not learning enough
### 1) Data Augmentation
"""
da_histories = {}
with strategy.scope():
aug_model = get_resnet_model([64,128,256,512], [3,4,6,3])
aug_model.compile(
optimizer=optimizer,
loss=[
tf.losses.CategoricalCrossentropy(),
tf.losses.CategoricalCrossentropy()
],
metrics= ['accuracy'])
da_histories['aug_model'] = aug_model.fit(
train_ds_aug,
epochs = epoch_size,
validation_data = (val_ds),
steps_per_epoch = len(X_train)//BATCH_SIZE,
validation_steps = len(X_val)//BATCH_SIZE,
verbose=0,
callbacks=get_callbacks('models/aug_model')
)
plotter(da_histories, ylim=[0.0, 10], metric = 'loss')
"""### 2) Regularization
* To find Regularization value I have used this custome function which will test out all the Hyper Parameters as argument one by one and print out plot of Lambda vs. Category Cross-entropy below.
"""
r_histories = {}
# lambda_vals = [1e-05, 1e-06, 2e-07,1e-07,5e-08,1e-08]
lambda_vals = [0.001 ,0.0001, 0.00001, 0.000002, 0.0000002, 0.0000005]
for reg_lambda in lambda_vals:
with strategy.scope():
tiny_res_net = get_resnet_model([64,128,256,512], [3,4,6,3], reg_lambda=reg_lambda)
tiny_res_net.compile(
optimizer=optimizer,
experimental_steps_per_execution = 50,
loss=[
tf.losses.CategoricalCrossentropy(),
tf.losses.CategoricalCrossentropy()
],
metrics= ['accuracy'])
r_histories['resnet_large_reg'+ '_h' + str(reg_lambda)] = tiny_res_net.fit(
train_ds_aug,
epochs = epoch_size,
validation_data = (val_ds),
steps_per_epoch = len(X_train)//BATCH_SIZE,
validation_steps = len(X_val)//BATCH_SIZE,
verbose=0,
callbacks=get_callbacks('models/resnet_large_reg'+ '_h1' + str(reg_lambda), early_stop=False))
plt.figure(figsize=(15,7))
metric = 'loss'
m_train = list()
m_val = list()
for reg_lambda in lambda_vals:
m_train.append(r_histories['resnet_large_reg'+ '_h' + str(reg_lambda)].history[metric][-1])
m_val.append(r_histories['resnet_large_reg'+ '_h' + str(reg_lambda)].history['val_' + metric][-1])
print(lambda_vals,m_train)
print(lambda_vals,m_val)
plt.plot(lambda_vals,m_train, 'ro', label='Train' )
plt.plot(lambda_vals,m_val, 'bs', label='Test' )
plt.xlabel('Lambda', fontsize=15)
plt.ylabel('Categorical Crossentropy', fontsize=14)
plt.legend()
plt.show()
"""From this plot we can see that 2e-07 is most efficient hyper parameter for Regularization value."""
rg_histories = {}
with strategy.scope():
resnet_regularization = get_resnet_model([64,128,256,512], [3,4,6,3], reg_lambda=2e-07)
resnet_regularization.compile(
optimizer=optimizer,
loss=[
tf.losses.CategoricalCrossentropy(),
tf.losses.CategoricalCrossentropy()
],
metrics= ['accuracy'])
rg_histories['resnet_regularization'] = resnet_regularization.fit(
train_ds_aug,
epochs = epoch_size,
validation_data = (val_ds),
steps_per_epoch = len(X_train)//BATCH_SIZE,
validation_steps = len(X_val)//BATCH_SIZE,
verbose=0,
callbacks=get_callbacks('models/resnet_regularization')
)
plotter(rg_histories, ylim=[0.0, 10], metric = 'loss')
"""### 3) Diffrent Models
Let's test this hyper-parameters with diffrent numbers of layers
"""
hm_histories = {}
with strategy.scope():
# tiny_res_net = get_resnet_model([64,128,256,512], [6,6,6,6], reg_lambda=2e-7, fdropout=0.5)
res_net_huge = get_resnet_model([64,128,256,512,1024,2048], [3,3,3,2,2,1], reg_lambda=2e-7)
res_net_huge.compile(
optimizer=optimizer,
loss=[
tf.losses.CategoricalCrossentropy(),
tf.losses.CategoricalCrossentropy()
],
metrics= ['accuracy'])
hm_histories['res_net_huge'] = res_net_huge.fit(
train_ds_aug,
epochs = epoch_size,
validation_data = (val_ds),
steps_per_epoch = len(X_train)//BATCH_SIZE,
validation_steps = len(X_val)//BATCH_SIZE,
verbose=0,
callbacks=get_callbacks('models/res_net_huge')
)
plotter(hm_histories, ylim=[0.0, 10], metric = 'loss')
m1_histories = {}
with strategy.scope():
resnet_model_huge1 = get_resnet_model([64,128,256,512,1024], [3,4,6,4,2], reg_lambda=2e-7)
resnet_model_huge1.compile(
optimizer=optimizer,
loss=[
tf.losses.CategoricalCrossentropy(),
tf.losses.CategoricalCrossentropy()
],
metrics= ['accuracy'])
m1_histories['resnet_model_huge1'] = resnet_model_huge1.fit(
train_ds_aug,
epochs = epoch_size,
validation_data = (val_ds),
steps_per_epoch = len(X_train)//BATCH_SIZE,
validation_steps = len(X_val)//BATCH_SIZE,
verbose=0,
callbacks=get_callbacks('models/resnet_model_huge1')
)
plotter(m1_histories, ylim=[0.0, 10], metric = 'loss')
m1_histories = {}
with strategy.scope():
resnet_model1 = get_resnet_model([64,128,256], [3,4,6], reg_lambda=2e-7)
resnet_model1.compile(
optimizer=optimizer,
loss=[
tf.losses.CategoricalCrossentropy(),
tf.losses.CategoricalCrossentropy()
],
metrics= ['accuracy'])
m1_histories['resnet_model1'] = resnet_model1.fit(
train_ds_aug,
epochs = epoch_size,
validation_data = (val_ds),
steps_per_epoch = len(X_train)//BATCH_SIZE,
validation_steps = len(X_val)//BATCH_SIZE,
verbose=0,
callbacks=get_callbacks('models/resnet_model1')
)
plotter(m1_histories, ylim=[0.0, 10], metric = 'loss')
"""From this we can esily say that for tilt, going deeper into layers with [64,128,256,512], [3,4,6,3], reg_lambda=2e-07 has best loss function.
## Final Models
### Model for Tilt
"""
final_histories = {}
with strategy.scope():
resnet_final = get_resnet_model([64,128,256,512], [3,4,6,3], reg_lambda=2e-07)
resnet_final.compile(
optimizer=optimizer,
loss=[
tf.losses.CategoricalCrossentropy(),
tf.losses.CategoricalCrossentropy()
],
metrics= ['accuracy'])
final_histories['resnet_final'] = resnet_final.fit(
train_ds_aug,
epochs = epoch_size,
validation_data = (val_ds),
steps_per_epoch = len(X_train)//BATCH_SIZE,
validation_steps = len(X_val)//BATCH_SIZE,
verbose=0,
callbacks=get_callbacks('models/resnet_final')
)
plotter(final_histories, ylim=[0.0, 10], metric = 'loss')
"""## Predicting
Let's see how our final model is performing on full dataset with compared to other few models.
"""
print(resnet_model_huge1.evaluate(full_train_ds_aug))
print(resnet_model1.evaluate(full_train_ds_aug))
print(resnet_final.evaluate(full_train_ds_aug))
"""This function is created to predict angles of Images"""
def show_prediction(title):
# Get movie info
img_path = os.path.join(images_dir, str(title))
# Read and prepare image
img = tf.keras.preprocessing.image.load_img(img_path, target_size=(144,192,3))
img = tf.keras.preprocessing.image.img_to_array(img)
img = img/255
img = np.expand_dims(img, axis=0)
# Generate prediction
prediction = resnet_final.predict(img)
predicted1 = np.argwhere(prediction[0] > 0.5).astype('int')
predicted2 = np.argwhere(prediction[1] > 0.5).astype('int')
prediction1 = pd.Series([-90, -60, -30, -15, 0, +15, +30, +60, +90])
prediction2 = pd.Series([ -90, -75, -60, -45, -30, -15, 0, +15, +30, +45, +60, +75, +90])
filename = title
try:
tilt = prediction1[predicted1[0][1]]
except:
tilt = None
try:
pan = prediction2[predicted2[0][1]]
except:
pan = None
return [str(filename), tilt, pan]
titles = np.array(X_test)
test_predict = pd.DataFrame(columns=['filename', 'tilt', 'pan'])
data = []
for i in titles:
data.append(show_prediction(i))
df = pd.DataFrame(data, columns=['filename', 'tilt', 'pan'])
df.head(10)
"""Let's see our model's prediction"""
plt.figure(figsize=(10,10))
for i in range(16):
plt.subplot(4,4,i+1)
plt.xticks([])
plt.yticks([])
plt.grid(False)
image = mpimg.imread(str(images_dir)+'/'+df['filename'].iloc[i]) # images are color images
plt.imshow(image)
plt.title(df['filename'].iloc[i])
plt.xlabel([ str(df['tilt'].iloc[i])+' || '+str(df['pan'].iloc[i]) ])
plt.show()
"""Let's check how many prediction were filled with NAN"""
df.tilt.value_counts(dropna=False)
df.pan.value_counts(dropna=False)
"""For the marking porpose of assignment I have filled NAN with 0's"""
df = df.fillna(0)
df.head()
df.to_csv('s3806853_predictions.csv', encoding='utf-8')
from IPython.display import HTML
import pandas as pd
import numpy as np
import base64
# function that takes in a dataframe and creates a text link to
# download it (will only work for files < 2MB or so)
def create_download_link(df, title = "Download CSV file", filename = "data.csv"):
csv = df.to_csv()
b64 = base64.b64encode(csv.encode())
payload = b64.decode()
html = '<a download="{filename}" href="data:text/csv;base64,{payload}" target="_blank">{title}</a>'
html = html.format(payload=payload,title=title,filename=filename)
return HTML(html)
create_download_link(df)