Image Classification with Keras CNN

img_cnn.py

import numpy as np # linear algebra
import pandas as pd # data processing, CSV file I/O (e.g. pd.read_csv)
import os
from PIL import Image
import matplotlib.pyplot as plt
from matplotlib.pyplot import imshow
from skimage import transform
from sklearn.model_selection import train_test_split
import traceback
from sklearn.utils import shuffle
from keras.models import Sequential,Input,Model
from keras.layers import Dense, Dropout, Flatten
from keras.layers import Conv2D, MaxPooling2D
from keras.layers.normalization import BatchNormalization
from keras.layers.advanced_activations import LeakyReLU
from keras.optimizers import Adam, SGD
from keras.callbacks import EarlyStopping

def imgToDF(path):
  """Takes the root directory for the categorical subdirectories of images and returns a dataframe classifying each path.
    For example path may contain several directories:
    Cat, Dog, etc.
    The function will take path and return:
    Path                Type
    C:/../cat1.jpg      Cat
    C:/../cat2.jpg      Cat
    ...
    C:/../dog1.jpg      Dog
  """
    df = []
    for i in os.listdir(path):
        img_path = path + '/{}'.format(i)
        img_list = os.listdir(img_path)
        img_cat = [(path + '/' + i + '/' + x, i) for x in img_list]
        df += img_cat
    df = pd.DataFrame.from_records(df, index = list(range(len(df))), columns=['path', 'type'])
    return df

def mk_img_array(df, img_size=(100,100)):
  """Take each path generated by imgToDF and construct X data comprised of numpy arrays of dimensions img_size"""
    X = []
    for i in df['path']:
        img = Image.open(i)
        img.load()
        img = np.asarray(img, dtype='float32')
        img = img/255
        data = transform.resize(img,img_size)
        X.append(data)
    return np.array(X)
  
def simple_CNN(input_shape, nodes_per=[32,64], hidden=1, out=2, act_out='softmax', act_hid='relu', drop=True, d_rate=0.1):
  """generate a simple CNN using Keras with variable architecture and print out model summary"""
    model = Sequential()
    model.add(Conv2D(32, (3, 3), padding='same', activation=act_hid, input_shape=input_shape))
    model.add(MaxPooling2D(pool_size=(2, 2)))
    if drop:
        model.add(Dropout(d_rate))
    try:
        if hidden != 0:
            for i,j in zip(range(hidden), nodes_per[1:]):
                model.add(Conv2D(64, (3, 3), padding='same', activation=act_hid))
                model.add(MaxPooling2D(pool_size=(2, 2)))
                if drop:
                    model.add(Dropout(d_rate))
        model.add(Flatten())
        model.add(Dense(512, activation=act_hid))
        model.add(Dropout(0.5))
        model.add(Dense(out, activation=act_out))
        print(model.summary())
        return model
    except:
        print(traceback.format_exc())
        print('Error in generating hidden layers')