Deep learning a pilot project to identify cats and dogs

Tang teacher of cats and dogs from project identification and project data set.

Item specific implementation steps:

1. Read the training data set 500 cats +500.

2. The processing of the picture read, processed into a uniform size format, divided into many labels.

3.shuffle about the cats and dogs doping mixed data, random as possible.

4. Using CNN network training test.

Specific code as follows:

1. Read the training set.

import pandas as pd
import numpy as np
import os
import glob
import matplotlib.pyplot as plt
import cv2 as cv2

images = []
labels = []
img_names = []
cls = []
train_path="training_data"
classes = ['dogs','cats']
num_classes = len(classes)
image_size=128
print('Going to read training images')
for fields in classes:   
    index = classes.index(fields)
    print('Now going to read {} files (Index: {})'.format(fields, index))
    path = os.path.join(train_path, fields, '*g')
    files = glob.glob(path)
    print(len(files))
    for fl in files:
        image = cv2.imread(fl)
        image = cv2.resize(image, (image_size, image_size),0,0, cv2.INTER_LINEAR)
        image = image.astype(np.float32)
        image = np.multiply(image, 1.0 / 255.0)
        images.append(image)
        label = np.zeros(len(classes))
        label[index] = 1.0
        labels.append(label)
        flbase = os.path.basename(fl)
        img_names.append(flbase)
        cls.append(fields)
images = np.array(images)
labels = np.array(labels)
img_names = np.array(img_names)
cls = np.array(cls)

2. The training data set.

from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense,Dropout,Flatten,Conv2D,MaxPool2D
from tensorflow.keras import Input
from sklearn.model_selection import train_test_split
from sklearn.utils import shuffle
from tensorflow.keras.regularizers import l1

# Dataset processor 
Images, Labels = shuffle (Images, Labels) 
X_train, X_test, y_train, android.permission.FACTOR. = Train_test_split (Images, Labels)

= Model the Sequential ()
 # first layer convolution, the convolution kernel 32, the size of 5x5, convolution mode SAME, RELU activation function, the magnitude of the input tensor 
model.add (Conv2D (filters = 6, kernel_size = (3 ,. 3), padding = ' Valid ' , kernel_regularizer L1 = (0.1), Activation = ' tanh ' , input_shape = (128,128,3 )))
 # model.add (Conv2D (= Filters 32, kernel_size = (3,3) , padding = 'Valid', Activation = 'RELU')) 
# pooled layer, pooling nuclear size 2x2 
model.add (MaxPool2D (pool_size = (2,2 & )))
 # random drop quarter of network connections, to prevent overfitting 
model.add (Dropout (0.5 )) 
model.add (Conv2D (Filters =. 6, kernel_size = (3,3), padding = ' Same, ' , = Activation' Tanh ' ))
 # model.add (Conv2D (= Filters. 6, kernel_size = (3,3), padding = 'Same,', Activation = 'tanh')) 
model.add (MaxPool2D (pool_size = (2,2 &) , Strides = (2,2 & ))) 
model.add (Dropout ( 0.5 ))
 # fully connected layers, unfolding operation, 
model.add (the Flatten ())
 # add a hidden layer neuron activation function and number 
# model.add (the Dense (120, Activation = 'tanh'))    
model.add (Dropout (0.5 ))
 # model.add (the Dense (84, Activation = 'tanh'))    
# output layer 
model.add (Dense (2, activation = ' SoftMax ' ))

model.compile(loss='categorical_crossentropy',
              optimizer='adam',
              metrics=['accuracy'])

model.fit(images,labels,validation_split=0.2,batch_size=128,epochs=50,c)

# Model storage 
MP = " model_3_1625.h5 " 
model.save (MP)

# Model Evaluation 
model.evaluate (X_test, y_test)

Training process feedback is as follows:

Deep learning a pilot project to identify cats and dogs

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