deep_learning_with

import tensorflow as tf from tensorflow import keras import tensorflow_datasets as tfds import numpy as np import matplotlib.pyplot as plt

# Print the version of tf, and tfds print("TensorFlow's version used in notebook:{}".format(tf.__version__)) print("TensorFlow datasets's version used in notebook:{}".format(tfds.__version__))

TensorFlow's version used in notebook:2.5.0
TensorFlow datasets's version used in notebook:4.0.1

# (train_data, train_label), (test_data, test_label) = keras.datasets.cifar10.load_data()

(train_data, val_data, test_data), info = tfds.load('cifar10', split=['train', 'test[:50%]','test[50%:]'], as_supervised=True, shuffle_files=False, with_info=True )

Downloading and preparing dataset cifar10/3.0.2 (download: 162.17 MiB, generated: 132.40 MiB, total: 294.58 MiB) to /root/tensorflow_datasets/cifar10/3.0.2...

IPyWidgets are not supported

Shuffling and writing examples to /root/tensorflow_datasets/cifar10/3.0.2.incompleteDB9DNF/cifar10-train.tfrecord

IPyWidgets are not supported

Shuffling and writing examples to /root/tensorflow_datasets/cifar10/3.0.2.incompleteDB9DNF/cifar10-test.tfrecord

IPyWidgets are not supported

Dataset cifar10 downloaded and prepared to /root/tensorflow_datasets/cifar10/3.0.2. Subsequent calls will reuse this data.

print("The number images in training set: {}".format(len(train_data))) print("The number images in validation set: {}".format(len(val_data))) print("The number images in test set: {}".format(len(test_data)))

The number images in training set: 50000
The number images in validation set: 5000
The number images in test set: 5000

# Getting the number of examples info.splits['train'].num_examples

# Getting the number of examples info.splits['test'].num_examples

# getting class names info.features['label'].names

# Number of classes info.features['label'].num_classes

fig = tfds.show_examples(train_data, info)

def preprocess(image, label): """ Take image and label, convert images to float32, and return converted image &label """ normalized_img = tf.cast(image, tf.float32)/255.0 return normalized_img, label

def train_data_prep(data, shuffle_size, batch_size): data = data.map(preprocess) data = data.cache() data = data.shuffle(shuffle_size).repeat() data = data.batch(batch_size) data = data.prefetch(1) return data

def test_data_prep(data, batch_size): data = data.map(preprocess) data = data.batch(batch_size) data = data.cache() data = data.prefetch(1) return data

train_data_prepared = train_data_prep(train_data, 1000, 32)

test_data_prepared = test_data_prep(test_data, 32)

val_data_prepared = test_data_prep(val_data, 32)

input_shape = [32,32,3] cifar_cnn = tf.keras.models.Sequential([ # First Convolutional layer: 64 filters, kernel/filter size of 3 tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), activation='relu', input_shape=input_shape), # First Pooling layer tf.keras.layers.MaxPooling2D(pool_size=2), # Second Convolutional layer & Pooling tf.keras.layers.Conv2D(filters=128, kernel_size=(3,3), activation='relu'), tf.keras.layers.MaxPooling2D(pool_size=2), # Third Convolutional & Pooling layer tf.keras.layers.Conv2D(filters=256, kernel_size=(3,3), activation='relu'), tf.keras.layers.MaxPooling2D(pool_size=2), # Flattening layer: For converting previous output into 1D column vector tf.keras.layers.Flatten(), # Fully Connected layers tf.keras.layers.Dense(units=128, activation='relu'), tf.keras.layers.Dense(units=64, activation='relu'), # Last layer: 10 neurons for 10 classes, activated by softmax tf.keras.layers.Dense(units=10, activation='softmax') ])

# Model summary cifar_cnn.summary()

Model: "sequential_1"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_3 (Conv2D)            (None, 30, 30, 64)        1792      
_________________________________________________________________
max_pooling2d_3 (MaxPooling2 (None, 15, 15, 64)        0         
_________________________________________________________________
conv2d_4 (Conv2D)            (None, 13, 13, 128)       73856     
_________________________________________________________________
max_pooling2d_4 (MaxPooling2 (None, 6, 6, 128)         0         
_________________________________________________________________
conv2d_5 (Conv2D)            (None, 4, 4, 256)         295168    
_________________________________________________________________
max_pooling2d_5 (MaxPooling2 (None, 2, 2, 256)         0         
_________________________________________________________________
flatten_1 (Flatten)          (None, 1024)              0         
_________________________________________________________________
dense_3 (Dense)              (None, 128)               131200    
_________________________________________________________________
dense_4 (Dense)              (None, 64)                8256      
_________________________________________________________________
dense_5 (Dense)              (None, 10)                650       
=================================================================
Total params: 510,922
Trainable params: 510,922
Non-trainable params: 0
_________________________________________________________________

tf.keras.utils.plot_model(cifar_cnn, rankdir='LR',show_dtype=True)

cifar_cnn.compile(optimizer='adam', loss='sparse_categorical_crossentropy', metrics=['accuracy'])

batch_size = 32 train_size = info.splits["train"].num_examples val_size = len(val_data_prepared) train_steps = int(train_size/batch_size) val_steps = int(val_size/batch_size) model_history = cifar_cnn.fit(train_data_prepared, validation_data=val_data_prepared, epochs=9, steps_per_epoch=train_steps, validation_steps=val_steps)

Epoch 1/9
1562/1562 [==============================] - 8s 5ms/step - loss: 1.0155 - accuracy: 0.6438 - val_loss: 1.1868 - val_accuracy: 0.5859
Epoch 2/9
1562/1562 [==============================] - 8s 5ms/step - loss: 0.8516 - accuracy: 0.7007 - val_loss: 0.9781 - val_accuracy: 0.6484
Epoch 3/9
1562/1562 [==============================] - 8s 5ms/step - loss: 0.7368 - accuracy: 0.7428 - val_loss: 0.9412 - val_accuracy: 0.6797
Epoch 4/9
1562/1562 [==============================] - 8s 5ms/step - loss: 0.6447 - accuracy: 0.7745 - val_loss: 0.8999 - val_accuracy: 0.6719
Epoch 5/9
1562/1562 [==============================] - 7s 5ms/step - loss: 0.5623 - accuracy: 0.8032 - val_loss: 0.9244 - val_accuracy: 0.6641
Epoch 6/9
1562/1562 [==============================] - 7s 5ms/step - loss: 0.5041 - accuracy: 0.8230 - val_loss: 1.0261 - val_accuracy: 0.6953
Epoch 7/9
1562/1562 [==============================] - 7s 5ms/step - loss: 0.4413 - accuracy: 0.8441 - val_loss: 1.0780 - val_accuracy: 0.6562
Epoch 8/9
1562/1562 [==============================] - 7s 5ms/step - loss: 0.3939 - accuracy: 0.8621 - val_loss: 1.0975 - val_accuracy: 0.7031
Epoch 9/9
1562/1562 [==============================] - 7s 5ms/step - loss: 0.3516 - accuracy: 0.8763 - val_loss: 1.1452 - val_accuracy: 0.7188

import pandas as pd # Getting the model training history model_history = model_history.history # Model history into a pandas dataframe loss_acc_df = pd.DataFrame(model_history) # Plotting the loss and accuracy loss_acc_df.plot(figsize=(10,5))

acc = model_history['accuracy'] val_acc = model_history['val_accuracy'] loss = model_history['loss'] val_loss = model_history['val_loss'] epochs = cifar_cnn.history.epoch # function to plot accuracy def plot_acc(acc, val_acc, epochs): plt.figure(figsize=(10,5)) plt.plot(epochs, acc, 'r', label='Training Accuracy') plt.plot(epochs, val_acc, 'g', label='Validation Accuracy') plt.title('Training and validation accuracy') plt.legend(loc=0) plt.figure() def plot_loss(loss, val_loss, epochs): plt.figure(figsize=(10,5)) plt.plot(epochs, loss, 'b', label='Training Loss') plt.plot(epochs, val_loss, 'y', label='Validation Loss') plt.title('Training and Validation Loss') plt.legend(loc=0) plt.figure()

#Plotting acc plot_acc(acc, val_acc, epochs)

plot_loss(loss, val_loss, epochs)

# reloading the data # Training set = 80% of orginal training set (50,000 images) # Validation set = 20% of training set = 10,000 images # Test set = full orginal test set = 10,000 images (train_data, val_data, test_data), info = tfds.load('cifar10', split=['train[:80%]', 'train[80%:]','test'], as_supervised=True, shuffle_files=True, with_info=True )

train_final = train_data_prep(train_data, 1000, 32) val_final = test_data_prep(val_data, 32) test_final = test_data_prep(test_data, 32)

input_shape = [32,32,3] cifar_cnn_2 = tf.keras.models.Sequential([ tf.keras.layers.Conv2D(filters=64, kernel_size=(3,3), padding='same',activation='relu', input_shape=input_shape), tf.keras.layers.MaxPooling2D(pool_size=2), tf.keras.layers.Conv2D(filters=128, kernel_size=(3,3), padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(pool_size=2), tf.keras.layers.Conv2D(filters=256, kernel_size=(3,3), padding='same', activation='relu'), tf.keras.layers.MaxPooling2D(pool_size=2), tf.keras.layers.Flatten(), tf.keras.layers.Dropout(0.5), tf.keras.layers.Dense(units=128, activation='relu'), tf.keras.layers.Dropout(0.5), tf.keras.layers.Dense(units=10, activation='softmax') ]) # Nadam optimizer is a version of adam cifar_cnn_2.compile(loss='sparse_categorical_crossentropy', optimizer='nadam', metrics='accuracy')

# Training the model for 10 epochs batch_size = 32 train_size = 40000 val_size = 10000 train_steps = int(train_size/batch_size) val_steps = int(val_size/batch_size) model_history_2 = cifar_cnn_2.fit(train_final, validation_data=val_final, epochs=10, steps_per_epoch=train_steps, validation_steps=val_steps)

Epoch 1/10
1250/1250 [==============================] - 8s 6ms/step - loss: 1.6176 - accuracy: 0.4100 - val_loss: 1.2067 - val_accuracy: 0.5677
Epoch 2/10
1250/1250 [==============================] - 7s 6ms/step - loss: 1.2107 - accuracy: 0.5696 - val_loss: 0.9822 - val_accuracy: 0.6585
Epoch 3/10
1250/1250 [==============================] - 7s 6ms/step - loss: 1.0364 - accuracy: 0.6356 - val_loss: 0.8793 - val_accuracy: 0.6897
Epoch 4/10
1250/1250 [==============================] - 7s 6ms/step - loss: 0.9407 - accuracy: 0.6689 - val_loss: 0.7914 - val_accuracy: 0.7224
Epoch 5/10
1250/1250 [==============================] - 8s 6ms/step - loss: 0.8663 - accuracy: 0.7006 - val_loss: 0.7671 - val_accuracy: 0.7341
Epoch 6/10
1250/1250 [==============================] - 8s 6ms/step - loss: 0.8183 - accuracy: 0.7145 - val_loss: 0.7386 - val_accuracy: 0.7490
Epoch 7/10
1250/1250 [==============================] - 8s 6ms/step - loss: 0.7659 - accuracy: 0.7297 - val_loss: 0.7462 - val_accuracy: 0.7378
Epoch 8/10
1250/1250 [==============================] - 7s 6ms/step - loss: 0.7292 - accuracy: 0.7430 - val_loss: 0.7406 - val_accuracy: 0.7443
Epoch 9/10
1250/1250 [==============================] - 8s 6ms/step - loss: 0.6991 - accuracy: 0.7552 - val_loss: 0.7469 - val_accuracy: 0.7402
Epoch 10/10
1250/1250 [==============================] - 8s 6ms/step - loss: 0.6683 - accuracy: 0.7652 - val_loss: 0.7016 - val_accuracy: 0.7586

model_history = model_history_2.history acc = model_history['accuracy'] val_acc = model_history['val_accuracy'] loss = model_history['loss'] val_loss = model_history['val_loss'] epochs = model_history_2.epoch

# Plot accuracy plot_acc(acc, val_acc, epochs)

# Plot loss plot_loss(loss, val_loss, epochs)

# Evaluating a model on test set loss, acc = cifar_cnn_2.evaluate(test_final)

313/313 [==============================] - 2s 7ms/step - loss: 0.7157 - accuracy: 0.7552

# Print loss and acc with 3 only decimal points print('Test Loss: {:.3f}'.format(loss)) print('Test Accuracy: {:.3f}'.format(acc))

Test Loss: 0.716
Test Accuracy: 0.755

# Saving a model cifar_cnn_2.save('Cifar Best.h5')

# Loading a saved model from keras.models import load_model load_cifar = load_model('Cifar Best.h5')

load_cifar.summary()

Model: "sequential_8"
_________________________________________________________________
Layer (type)                 Output Shape              Param #   
=================================================================
conv2d_24 (Conv2D)           (None, 32, 32, 64)        1792      
_________________________________________________________________
max_pooling2d_24 (MaxPooling (None, 16, 16, 64)        0         
_________________________________________________________________
conv2d_25 (Conv2D)           (None, 16, 16, 128)       73856     
_________________________________________________________________
max_pooling2d_25 (MaxPooling (None, 8, 8, 128)         0         
_________________________________________________________________
conv2d_26 (Conv2D)           (None, 8, 8, 256)         295168    
_________________________________________________________________
max_pooling2d_26 (MaxPooling (None, 4, 4, 256)         0         
_________________________________________________________________
flatten_8 (Flatten)          (None, 4096)              0         
_________________________________________________________________
dropout_10 (Dropout)         (None, 4096)              0         
_________________________________________________________________
dense_19 (Dense)             (None, 128)               524416    
_________________________________________________________________
dropout_11 (Dropout)         (None, 128)               0         
_________________________________________________________________
dense_20 (Dense)             (None, 10)                1290      
=================================================================
Total params: 896,522
Trainable params: 896,522
Non-trainable params: 0
_________________________________________________________________