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CNN.py
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CNN.py
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"""
@author: Pranav-Jain
"""
#CNN to detect plant diseases in Maize plant leaves
#Train accuracy ~ 96.2% and Test accuracy ~ 93%
import tensorflow
from tensorflow.keras.layers import Conv2D, Flatten, Dense, Dropout,MaxPooling2D,Flatten, BatchNormalization,SpatialDropout2D
from tensorflow.keras.models import Model,Sequential
model=Sequential()
model.add(Conv2D(32,(3,3),padding='same',activation='relu',input_shape=(150,150,3)))
model.add(MaxPooling2D(2,2))
#model.add(SpatialDropout2D(0.1))
model.add(Conv2D(32,(3,3),padding='same',activation='relu'))
model.add(MaxPooling2D(2,2))
model.add(BatchNormalization())
#model.add(SpatialDropout2D(0.2))
model.add(Conv2D(32,(3,3),padding='same',activation='relu'))
model.add(MaxPooling2D(2,2))
model.add(BatchNormalization())
#model.add(SpatialDropout2D(0.2))
model.add(Conv2D(32,(3,3),padding='same',activation='relu'))
model.add(MaxPooling2D(2,2))
model.add(BatchNormalization())
#model.add(SpatialDropout2D(0.3))
model.add(Conv2D(32,(3,3),padding='same',activation='relu'))
model.add(MaxPooling2D(2,2))
model.add(BatchNormalization())
#model.add(SpatialDropout2D(0.3))
model.add(Conv2D(32,(3,3),padding='same',activation='relu'))
model.add(MaxPooling2D(2,2))
model.add(BatchNormalization())
#model.add(SpatialDropout2D(0.3))
model.add(Conv2D(32,(3,3),padding='same',activation='relu'))
model.add(MaxPooling2D(2,2))
model.add(BatchNormalization())
#model.add(SpatialDropout2D(0.5))
model.add(Flatten())
#model.add(Dropout(0.5))
model.add(Dense(128,activation='relu'))
#model.add(Dropout(0.5))
model.add(Dense(128,activation='relu'))
model.add(Dense(32,activation='relu'))
#model.add(Dropout(0.5))
model.add(Dense(4,activation='softmax'))
model.summary()
#adam=tensorflow.keras.optimizers.Adam(learning_rate=0.001)
model.compile(optimizer="Adam",loss='categorical_crossentropy',metrics=['accuracy',tensorflow.keras.metrics.Precision(),tensorflow.keras.metrics.Recall(),tensorflow.keras.metrics.AUC()])
from tensorflow.keras.preprocessing.image import ImageDataGenerator
train_datagen = ImageDataGenerator(
rescale=1./255,
shear_range=0.2,
zoom_range=0.2,
horizontal_flip=True)
test_datagen = ImageDataGenerator(rescale=1./255)
training_set = train_datagen.flow_from_directory(
'/content/drive/MyDrive/datasamenoofpics/train',
target_size=(150,150),
batch_size=32 ,
class_mode='categorical')
val_set = test_datagen.flow_from_directory(
'/content/drive/MyDrive/datasamenoofpics/test',
target_size=(150,150),
batch_size=32,
class_mode='categorical')
history=model.fit_generator(
training_set,
steps_per_epoch=12,
epochs=10,
validation_data=val_set,
validation_steps=15)
from google.colab import drive
drive.mount('/content/drive')
import matplotlib.pyplot as plt
plt.subplot(211)
plt.plot(history.history['accuracy'])
plt.plot(history.history['val_accuracy'])
plt.title('model accuracy')
plt.ylabel('accuracy')
plt.xlabel('epoch')
plt.legend(['train', 'test'], loc='upper left')