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Exploring How Convolutions and Pooling work

Posted on Edited on In , Valine:
Exploring how Convolutions and Pooling work
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import os

from tensorflow.keras import layers
from tensorflow.keras import Model
!wget --no-check-certificate \
    https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5 \
    -O /tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
  
from tensorflow.keras.applications.inception_v3 import InceptionV3

local_weights_file = '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'

pre_trained_model = InceptionV3(input_shape = (150, 150, 3), 
                                include_top = False, 
                                weights = None)

pre_trained_model.load_weights(local_weights_file)

for layer in pre_trained_model.layers:
  layer.trainable = False
  
# pre_trained_model.summary()

last_layer = pre_trained_model.get_layer('mixed7')
print('last layer output shape: ', last_layer.output_shape)
last_output = last_layer.output
--2019-02-13 14:04:55--  https://storage.googleapis.com/mledu-datasets/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5
Resolving storage.googleapis.com... 2607:f8b0:4003:c05::80, 64.233.168.128
Connecting to storage.googleapis.com|2607:f8b0:4003:c05::80|:443... connected.
WARNING: cannot verify storage.googleapis.com's certificate, issued by 'CN=Google Internet Authority G3,O=Google Trust Services,C=US':
  Unable to locally verify the issuer's authority.
HTTP request sent, awaiting response... 200 OK
Length: 87910968 (84M) [application/x-hdf]
Saving to: '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5'

/tmp/inception_v3_w 100%[=====================>]  83.84M  75.6MB/s   in 1.1s   

2019-02-13 14:04:56 (75.6 MB/s) - '/tmp/inception_v3_weights_tf_dim_ordering_tf_kernels_notop.h5' saved [87910968/87910968]

('last layer output shape: ', (None, 7, 7, 768))
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from tensorflow.keras.optimizers import RMSprop

# Flatten the output layer to 1 dimension
x = layers.Flatten()(last_output)
# Add a fully connected layer with 1,024 hidden units and ReLU activation
x = layers.Dense(1024, activation='relu')(x)
# Add a dropout rate of 0.2
x = layers.Dropout(0.2)(x)                  
# Add a final sigmoid layer for classification
x = layers.Dense  (1, activation='sigmoid')(x)           

model = Model( pre_trained_model.input, x) 

model.compile(optimizer = RMSprop(lr=0.0001), 
              loss = 'binary_crossentropy', 
              metrics = ['acc'])

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!wget --no-check-certificate \
        https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip \
       -O /tmp/cats_and_dogs_filtered.zip

from tensorflow.keras.preprocessing.image import ImageDataGenerator

import os
import zipfile

local_zip = '//tmp/cats_and_dogs_filtered.zip'

zip_ref = zipfile.ZipFile(local_zip, 'r')

zip_ref.extractall('/tmp')
zip_ref.close()

# Define our example directories and files
base_dir = '/tmp/cats_and_dogs_filtered'

train_dir = os.path.join( base_dir, 'train')
validation_dir = os.path.join( base_dir, 'validation')


train_cats_dir = os.path.join(train_dir, 'cats') # Directory with our training cat pictures
train_dogs_dir = os.path.join(train_dir, 'dogs') # Directory with our training dog pictures
validation_cats_dir = os.path.join(validation_dir, 'cats') # Directory with our validation cat pictures
validation_dogs_dir = os.path.join(validation_dir, 'dogs')# Directory with our validation dog pictures

train_cat_fnames = os.listdir(train_cats_dir)
train_dog_fnames = os.listdir(train_dogs_dir)

# Add our data-augmentation parameters to ImageDataGenerator
train_datagen = ImageDataGenerator(rescale = 1./255.,
                                   rotation_range = 40,
                                   width_shift_range = 0.2,
                                   height_shift_range = 0.2,
                                   shear_range = 0.2,
                                   zoom_range = 0.2,
                                   horizontal_flip = True)

# Note that the validation data should not be augmented!
test_datagen = ImageDataGenerator( rescale = 1.0/255. )

# Flow training images in batches of 20 using train_datagen generator
train_generator = train_datagen.flow_from_directory(train_dir,
                                                    batch_size = 20,
                                                    class_mode = 'binary', 
                                                    target_size = (150, 150))     

# Flow validation images in batches of 20 using test_datagen generator
validation_generator =  test_datagen.flow_from_directory( validation_dir,
                                                          batch_size  = 20,
                                                          class_mode  = 'binary', 
                                                          target_size = (150, 150))
--2019-02-13 14:05:24--  https://storage.googleapis.com/mledu-datasets/cats_and_dogs_filtered.zip
Resolving storage.googleapis.com... 2607:f8b0:4003:c0a::80, 173.194.223.128
Connecting to storage.googleapis.com|2607:f8b0:4003:c0a::80|:443... connected.
WARNING: cannot verify storage.googleapis.com's certificate, issued by 'CN=Google Internet Authority G3,O=Google Trust Services,C=US':
  Unable to locally verify the issuer's authority.
HTTP request sent, awaiting response... 200 OK
Length: 68606236 (65M) [application/zip]
Saving to: '/tmp/cats_and_dogs_filtered.zip'

/tmp/cats_and_dogs_ 100%[=====================>]  65.43M   168MB/s   in 0.4s   

2019-02-13 14:05:24 (168 MB/s) - '/tmp/cats_and_dogs_filtered.zip' saved [68606236/68606236]

Found 2000 images belonging to 2 classes.
Found 1000 images belonging to 2 classes.
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history = model.fit_generator(
            train_generator,
            validation_data = validation_generator,
            steps_per_epoch = 100,
            epochs = 20,
            validation_steps = 50,
            verbose = 2)
Epoch 1/20
100/100 - 17s - loss: 0.5283 - acc: 0.7525 - val_loss: 0.3843 - val_acc: 0.8940
Epoch 2/20
100/100 - 14s - loss: 0.3678 - acc: 0.8340 - val_loss: 0.2040 - val_acc: 0.9480
Epoch 3/20
100/100 - 15s - loss: 0.3352 - acc: 0.8535 - val_loss: 0.3987 - val_acc: 0.9270
Epoch 4/20
100/100 - 15s - loss: 0.3432 - acc: 0.8550 - val_loss: 0.2987 - val_acc: 0.9440
Epoch 5/20
100/100 - 15s - loss: 0.3391 - acc: 0.8640 - val_loss: 0.3390 - val_acc: 0.9450
Epoch 6/20
100/100 - 14s - loss: 0.3135 - acc: 0.8680 - val_loss: 0.3465 - val_acc: 0.9480
Epoch 7/20
100/100 - 14s - loss: 0.3113 - acc: 0.8700 - val_loss: 0.3115 - val_acc: 0.9530
Epoch 8/20
100/100 - 15s - loss: 0.2901 - acc: 0.8820 - val_loss: 0.5042 - val_acc: 0.9370
Epoch 9/20
100/100 - 15s - loss: 0.2912 - acc: 0.8865 - val_loss: 0.3065 - val_acc: 0.9620
Epoch 10/20
100/100 - 15s - loss: 0.2944 - acc: 0.8760 - val_loss: 0.2641 - val_acc: 0.9640
Epoch 11/20
100/100 - 14s - loss: 0.2831 - acc: 0.8810 - val_loss: 0.4515 - val_acc: 0.9450
Epoch 12/20
100/100 - 15s - loss: 0.2682 - acc: 0.8895 - val_loss: 0.3231 - val_acc: 0.9580
Epoch 13/20
100/100 - 15s - loss: 0.2748 - acc: 0.8840 - val_loss: 0.2427 - val_acc: 0.9680
Epoch 14/20
100/100 - 15s - loss: 0.2669 - acc: 0.8945 - val_loss: 0.3075 - val_acc: 0.9630
Epoch 15/20
100/100 - 15s - loss: 0.2732 - acc: 0.8910 - val_loss: 0.2629 - val_acc: 0.9620
Epoch 16/20
100/100 - 14s - loss: 0.2634 - acc: 0.8940 - val_loss: 0.3864 - val_acc: 0.9570
Epoch 17/20
100/100 - 14s - loss: 0.2473 - acc: 0.9040 - val_loss: 0.2648 - val_acc: 0.9670
Epoch 18/20
100/100 - 15s - loss: 0.2767 - acc: 0.8890 - val_loss: 0.2519 - val_acc: 0.9620
Epoch 19/20
100/100 - 17s - loss: 0.2660 - acc: 0.8990 - val_loss: 0.2495 - val_acc: 0.9680
Epoch 20/20
100/100 - 15s - loss: 0.2535 - acc: 0.9020 - val_loss: 0.2682 - val_acc: 0.9670
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import matplotlib.pyplot as plt
acc = history.history['acc']
val_acc = history.history['val_acc']
loss = history.history['loss']
val_loss = history.history['val_loss']

epochs = range(len(acc))

plt.plot(epochs, acc, 'r', label='Training accuracy')
plt.plot(epochs, val_acc, 'b', label='Validation accuracy')
plt.title('Training and validation accuracy')
plt.legend(loc=0)
plt.figure()


plt.show()
png 
<matplotlib.figure.Figure at 0x7ff19c530b90>