TF by DL.AI — Course 1: Intro to TF for AI, ML and DL

Basic DL on MNIST

Comments (notebook):

Adding more Neurons we have to do more calculations, slowing down the process, but get more accurate.

The first layer in your network should be the same shape as your data.

The number of neurons in the last layer should match the number of classes you are classifying for.

Extra layers are often necessary.

Flatten as the name implies, converts your multidimensional matrices (Batch.Size x Img.W x Img.H x Kernel.Size) to a nice single 2-dimensional matrix: (Batch.Size x (Img.W x Img.H x Kernel.Size)). During backpropagation it also converts back your delta of size (Batch.Size x (Img.W x Img.H x Kernel.Size)) to the original (Batch.Size x Img.W x Img.H x Kernel.Size).

Dense layer is of course the standard fully connected layer.

CNN layers, cource of image.

Basic DL on Fashion-MNIST

Basic CNN on Fashion-MNIST

Refs:

Kernel in image processing: examples with images.

Pooling layer: non-linear down-sampling.

More?

Image Filtering — Lode's Computer Graphics Tutorial

Applying Convolutions on top of our Deep neural network will make training → It depends on many factors. It might make your training faster or slower, and a poorly designed Convolutional layer may even be less efficient than a plain DNN!

What is a Convolution? → A technique to isolate features in images

What is a Pooling? → A technique to reduce the information in an image while maintaining features

How do Convolutions improve image recognition? → They isolate features in images

After passing a 3x3 conv filter over a 28x28 image, how big will the output be? → 26x26{:.img-30}

7x7 to 5x5 (source)

After max pooling a 26x26 image with a 2x2 filter, how big will the output be? → 13x13

(source)

Visualizing the Convolutions and Pooling

Using layer API, something like below, check more in the notebook.

Using real-world images

An example of classifying horses and humans!

ImageGenerator

👉 Video explain ImageGenerator.

ConvNet with ImageGenerator

More docs:

Understanding Categorical Cross-Entropy Loss, Binary Cross-Entropy Loss, Softmax Loss, Logistic Loss, Focal Loss and all those confusing names

Overview of mini-batch gradient descent

1import tensorflow as tf 2 3# stop the training with condition 4class myCallback(tf.keras.callbacks.Callback): 5 def on_epoch_end(self, epoch, logs={}): # compare at the end of each epoch 6 if(logs.get('accuracy') > 0.99): 7 self.model.stop_training = True 8 9mnist = tf.keras.datasets.mnist 10(x_train, y_train),(x_test, y_test) = mnist.load_data() 11x_train, x_test = x_train / 255.0, x_test / 255.0 # normalize 12 13callbacks = myCallback() # define the callback 14 15model = tf.keras.models.Sequential([ 16 tf.keras.layers.Flatten(input_shape=(28, 28)), # Takes that square and 17 # turns it into a 1 dim 18 tf.keras.layers.Dense(512, activation=tf.nn.relu), 19 tf.keras.layers.Dense(10, activation=tf.nn.softmax) # 10 outputs 20]) 21 22model.compile(optimizer='adam', 23 loss='sparse_categorical_crossentropy', 24 metrics=['accuracy']) 25 26model.fit(x_train, y_train, epochs=10, callbacks=[callbacks])

1import tensorflow as tf 2mnist = tf.keras.datasets.fashion_mnist 3 4class myCallback(tf.keras.callbacks.Callback): 5 def on_epoch_end(self, epochs, logs={}) : 6 if(logs.get('accuracy') is not None and logs.get('accuracy') >= 0.998) : 7 print('\\nReached 99.8% accuracy so cancelling training!') 8 self.model.stop_training = True 9 10(training_images, training_labels), (test_images, test_labels) = mnist.load_data() 11# Why reshape? 12# The first convolution expects a single tensor containing everything, 13# so instead of 60000 28x28x1 items in a list, we have a single 4D list 14# that is 60000x28x28x1 15# 16# training_images' shape (before reshape): (60000, 28, 28) 17# training_images' shape (after reshape): (60000, 28, 28, 1) 18# trainaing_labels' shape: (60000,) 19training_images=training_images.reshape(60000, 28, 28, 1) 20training_images=training_images / 255.0 21test_images = test_images.reshape(10000, 28, 28, 1) 22test_images=test_images/255.0 23 24model = tf.keras.models.Sequential([ 25 tf.keras.layers.Conv2D(64, (3,3), activation='relu', input_shape=(28, 28, 1)), 26 tf.keras.layers.MaxPooling2D(2, 2), 27 tf.keras.layers.Conv2D(64, (3,3), activation='relu'), 28 tf.keras.layers.MaxPooling2D(2,2), 29 tf.keras.layers.Flatten(), 30 tf.keras.layers.Dense(128, activation='relu'), 31 tf.keras.layers.Dense(10, activation='softmax') 32]) 33 34model.compile(optimizer='adam', 35 loss='sparse_categorical_crossentropy', 36 metrics=['accuracy']) 37 38callbacks = myCallback() 39 40model.fit(training_images, training_labels, epochs=5, callbacks=[callbacks]) 41test_loss = model.evaluate(test_images, test_labels)

1Model: "sequential_1" 2_________________________________________________________________ 3Layer (type) Output Shape Param 4================================================================= 5conv2d (Conv2D) (None, 26, 26, 64) 640 6 # for every image, 64 convolution has been tried 7 # 26 (=28-2) because we use 3x3 filter and we can't 8 # count on edges, so the picture is 2 smaller on x and y. 9 # if 5x5 filter => 4 smaller on x and y. 10_________________________________________________________________ 11max_pooling2d (MaxPooling2D) (None, 13, 13, 64) 0 12_________________________________________________________________ 13conv2d_1 (Conv2D) (None, 11, 11, 64) 36928 14_________________________________________________________________ 15max_pooling2d_1 (MaxPooling2 (None, 5, 5, 64) 0 16_________________________________________________________________ 17flatten_1 (Flatten) (None, 1600) 0 18_________________________________________________________________ 19dense_2 (Dense) (None, 128) 204928 20_________________________________________________________________ 21dense_3 (Dense) (None, 10) 1290 22================================================================= 23Total params: 243,786 24Trainable params: 243,786 25Non-trainable params: 0

1import matplotlib.pyplot as plt 2f, axarr = plt.subplots() 3 4from tensorflow.keras import models 5layer_outputs = [layer.output for layer in model.layers] 6activation_model = tf.keras.models.Model(inputs = model.input, outputs = layer_outputs) 7 8for x in range(0,4): 9 f1 = activation_model.predict(test_images[FIRST_IMAGE].reshape(1, 28, 28, 1))[x] 10 axarr[0,x].imshow(f1[0, : , :, CONVOLUTION_NUMBER], cmap='inferno') 11 axarr[0,x].grid(False) 12 ...

1# make images more used for training 2# (focus on object, split cleary objects, label images,...) 3# also help to augmenting data (rotate, skew, flip,...) 4from tensorflow.keras.preprocessing.image import ImageDataGenerator 5 6train_datagen = ImageDataGenerator(rescale=1./255) 7 # normalize -> No need to convert images and then put in the training 8 # do the scaling on the fly 9train_generator = train_datagen.flow_from_directory( 10 train_dir, # dir contains the dir containing your images 11 # -> be careful! 12 target_size=(300, 300), # images will be resized when loaded, genial! 13 # because NN always needs that! 14 # -> experimenting with diff sizes without 15 # impacting your source data 16 batch_size=128, 17 class_mode="binary" # 2 diff things 18) 19 20test_datagen = ImageDataGenerator(rescale=1./255) # normalize 21validation_generator = test_datagen.flow_from_directory( 22 validation_dir, # dir contains the dir containing your images 23 target_size=(300, 300), 24 batch_size=32, 25 class_mode="binary" 26)