Hand_Written_Digit_Recognition_Using_ML-Deep_Learning_Approach-/intel image classification sample example.py at master · Mahamad-Jameer-Makandar/Hand_Written_Digit_Recognition_Using_ML-Deep_Learning_Approach- · GitHub

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#!/usr/bin/env python
# coding: utf-8

# In[1]:


import numpy as np
import matplotlib.pyplot as plt
import os

from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, Dense, MaxPooling2D, Activation, Dropout, BatchNormalization, Flatten
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.utils import to_categorical


# In[3]:


pip install tensorflow


# In[1]:


import numpy as np
import matplotlib.pyplot as plt
import os

from tensorflow import keras
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Conv2D, Dense, MaxPooling2D, Activation, Dropout, BatchNormalization, Flatten
from tensorflow.keras.preprocessing.image import ImageDataGenerator
from tensorflow.keras.utils import to_categorical


# In[4]:


train = ImageDataGenerator(rescale=1/255)
test = ImageDataGenerator(rescale=1/255)

train_dataset = train.flow_from_directory(directory='C:/Users/Jameer_Ibn_Hasan/Desktop/seg_train/seg_train',target_size=(50,50),shuffle=True)
test_dataset = test.flow_from_directory(directory='C:/Users/Jameer_Ibn_Hasan/Desktop/seg_test/seg_test',target_size=(50,50),shuffle=True)


# In[5]:


indices = [np.random.randint(32)for i in range(10)]
print(indices)

plt.figure(figsize = (20,8))
for i in enumerate(indices):
    plt.subplot(2,5,i[0]+1)
    plt.imshow(train_dataset[0][0][i[1]])
    plt.title(train_dataset[0][1][i[1]])
plt.show()


# In[10]:


values = list(train_dataset.class_indices.values())
keys = list(train_dataset.class_indices.keys())

dics = list(map(lambda x,y:{x:y},values,keys))

from functools import reduce
mappings = reduce(lambda x,y:{**x,**y},dics)
mappings
#print(values)
#print(keys)
#print(dics)


# In[18]:


model = Sequential()
model.add(Conv2D(filters=32,kernel_size=(3,3),padding='same',input_shape=(50,50,3)))
model.add(Activation('relu'))
model.add(Conv2D(filters=32,kernel_size=(3,3)))
model.add(Dropout(0.25))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))


model.add(Conv2D(filters=50,kernel_size=(3,3),padding='same',input_shape=(50,50,3)))
model.add(Activation('relu'))
model.add(Conv2D(filters=50,kernel_size=(3,3)))
model.add(Dropout(0.25))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))


model.add(Conv2D(filters=75,kernel_size=(3,3),padding='same',input_shape=(50,50,3)))
model.add(Activation('relu'))
model.add(Conv2D(filters=75,kernel_size=(3,3)))
model.add(Dropout(0.25))
model.add(Activation('relu'))
model.add(MaxPooling2D(pool_size=(2,2)))

model.add(Flatten())
kernel_regularizer = keras.regularizers.l1_l2(l1=1e-5,l2=1e-4)
model.add(Dense(units=50,activation='relu',kernel_regularizer=kernel_regularizer))
model.add(Dense(50,activation='relu'))
model.add(Dropout(0.25))
model.add(Dense(6,activation='softmax'))

model.summary()


# In[24]:


model.compile(loss='CategoricalCrossentropy',optimizer='adam',metrics='accuracy')
history = model.fit(train_dataset,batch_size=80,epochs=5,validation_data=test_dataset)


# In[30]:


plt.plot(history.history['accuracy'],label='accuracy')
plt.plot(history.history['val_accuracy'],label='val_accuracy')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')


# In[31]:


plt.plot(history.history['loss'],label='loss')
plt.plot(history.history['val_loss'],label='val_loss')
plt.xlabel('Epoch')
plt.ylabel('Accuracy')
plt.legend(loc='lower right')


# In[ ]: