Validation Accuracy shown during epochs much higher than what I actually get

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I am new to Keras and CNN and therefore struggling with the following.

When I train using image dataset using the follwing code:

train_batches = gen1.flow_from_directory(train_path, target_size=(224,224),classes = ['garbage', 'recycled', 'organic'], batch_size = batch)    



valid_batches = gen1.flow_from_directory(valid_path, target_size=(224,224), classes = ['garbage', 'recycled', 'organic'], batch_size = batch)



test_batches = gen1.flow_from_directory(test_path, target_size=(224,224), classes = ['garbage', 'recycled', 'organic'], batch_size = batch)



model = Sequential()

model.add(Conv2D(32, (3, 3), input_shape=( 224, 224, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(32, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(32, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(64, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(64, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors

model.add(Dense(64))

model.add(Activation('relu'))

model.add(Dropout(0.5))

model.add(Dense(3))

model.add(Activation('softmax'))



model.compile(optimizer=Adam(lr=.0001), loss= 'categorical_crossentropy', metrics = ['accuracy'])



history = model.fit_generator(train_batches, steps_per_epoch =training_data_size//batch, validation_data = valid_batches, validation_steps=validation_data_size//batch, epochs=5, verbose=2)

model.save_weights('Try1.h5')

I get the 77% validation accuracy after 5 epochs as can be seen in the pic :

enter image description here

But when I try to create a confusion matrix for it using I get 35% validation accuracy:

predictions = model.predict_generator(valid_batches, steps=validation_data_size//batch,verbose=1)

conf_mat2 = confusion_matrix(valid_batches.classes, np.argmax(predictions,axis=1))

asked Nov 11 at 15:47

vaibhav gupta

add a comment |

up vote
0
down vote

favorite

I am new to Keras and CNN and therefore struggling with the following.

When I train using image dataset using the follwing code:

train_batches = gen1.flow_from_directory(train_path, target_size=(224,224),classes = ['garbage', 'recycled', 'organic'], batch_size = batch)    



valid_batches = gen1.flow_from_directory(valid_path, target_size=(224,224), classes = ['garbage', 'recycled', 'organic'], batch_size = batch)



test_batches = gen1.flow_from_directory(test_path, target_size=(224,224), classes = ['garbage', 'recycled', 'organic'], batch_size = batch)



model = Sequential()

model.add(Conv2D(32, (3, 3), input_shape=( 224, 224, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(32, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(32, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(64, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(64, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors

model.add(Dense(64))

model.add(Activation('relu'))

model.add(Dropout(0.5))

model.add(Dense(3))

model.add(Activation('softmax'))



model.compile(optimizer=Adam(lr=.0001), loss= 'categorical_crossentropy', metrics = ['accuracy'])



history = model.fit_generator(train_batches, steps_per_epoch =training_data_size//batch, validation_data = valid_batches, validation_steps=validation_data_size//batch, epochs=5, verbose=2)

model.save_weights('Try1.h5')

I get the 77% validation accuracy after 5 epochs as can be seen in the pic :

enter image description here

But when I try to create a confusion matrix for it using I get 35% validation accuracy:

predictions = model.predict_generator(valid_batches, steps=validation_data_size//batch,verbose=1)

conf_mat2 = confusion_matrix(valid_batches.classes, np.argmax(predictions,axis=1))

asked Nov 11 at 15:47

vaibhav gupta

add a comment |

up vote
0
down vote

favorite

I am new to Keras and CNN and therefore struggling with the following.

When I train using image dataset using the follwing code:

train_batches = gen1.flow_from_directory(train_path, target_size=(224,224),classes = ['garbage', 'recycled', 'organic'], batch_size = batch)    



valid_batches = gen1.flow_from_directory(valid_path, target_size=(224,224), classes = ['garbage', 'recycled', 'organic'], batch_size = batch)



test_batches = gen1.flow_from_directory(test_path, target_size=(224,224), classes = ['garbage', 'recycled', 'organic'], batch_size = batch)



model = Sequential()

model.add(Conv2D(32, (3, 3), input_shape=( 224, 224, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(32, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(32, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(64, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(64, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors

model.add(Dense(64))

model.add(Activation('relu'))

model.add(Dropout(0.5))

model.add(Dense(3))

model.add(Activation('softmax'))



model.compile(optimizer=Adam(lr=.0001), loss= 'categorical_crossentropy', metrics = ['accuracy'])



history = model.fit_generator(train_batches, steps_per_epoch =training_data_size//batch, validation_data = valid_batches, validation_steps=validation_data_size//batch, epochs=5, verbose=2)

model.save_weights('Try1.h5')

I get the 77% validation accuracy after 5 epochs as can be seen in the pic :

enter image description here

But when I try to create a confusion matrix for it using I get 35% validation accuracy:

predictions = model.predict_generator(valid_batches, steps=validation_data_size//batch,verbose=1)

conf_mat2 = confusion_matrix(valid_batches.classes, np.argmax(predictions,axis=1))

asked Nov 11 at 15:47

vaibhav gupta

I am new to Keras and CNN and therefore struggling with the following.

When I train using image dataset using the follwing code:

train_batches = gen1.flow_from_directory(train_path, target_size=(224,224),classes = ['garbage', 'recycled', 'organic'], batch_size = batch)    



valid_batches = gen1.flow_from_directory(valid_path, target_size=(224,224), classes = ['garbage', 'recycled', 'organic'], batch_size = batch)



test_batches = gen1.flow_from_directory(test_path, target_size=(224,224), classes = ['garbage', 'recycled', 'organic'], batch_size = batch)



model = Sequential()

model.add(Conv2D(32, (3, 3), input_shape=( 224, 224, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(32, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(32, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(64, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Conv2D(64, (3, 3)))

model.add(Activation('relu'))

model.add(MaxPooling2D(pool_size=(2, 2)))



model.add(Flatten())  # this converts our 3D feature maps to 1D feature vectors

model.add(Dense(64))

model.add(Activation('relu'))

model.add(Dropout(0.5))

model.add(Dense(3))

model.add(Activation('softmax'))



model.compile(optimizer=Adam(lr=.0001), loss= 'categorical_crossentropy', metrics = ['accuracy'])



history = model.fit_generator(train_batches, steps_per_epoch =training_data_size//batch, validation_data = valid_batches, validation_steps=validation_data_size//batch, epochs=5, verbose=2)

model.save_weights('Try1.h5')

I get the 77% validation accuracy after 5 epochs as can be seen in the pic :

enter image description here

But when I try to create a confusion matrix for it using I get 35% validation accuracy:

predictions = model.predict_generator(valid_batches, steps=validation_data_size//batch,verbose=1)

conf_mat2 = confusion_matrix(valid_batches.classes, np.argmax(predictions,axis=1))

keras conv-neural-network

asked Nov 11 at 15:47

vaibhav gupta

asked Nov 11 at 15:47

vaibhav gupta

asked Nov 11 at 15:47

vaibhav gupta

asked Nov 11 at 15:47

vaibhav gupta

asked Nov 11 at 15:47

vaibhav gupta

add a comment |

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