Intro Keras

import tensorflow as tf
from tensorflow import keras
import numpy as np
from sklearn.datasets import make_classification
import matplotlib.pyplot as plt
print(f'Tensorflow version = {tf.__version__}')
print(f'Keras version = {keras.__version__}')

np.random.seed(0) # pick the seed for reproducibility - change it to explore the effects of random variations

n = 100
X, Y = make_classification(n_samples = n, n_features=2,
                           n_redundant=0, n_informative=2, flip_y=0.05,class_sep=1.5)
X = X.astype(np.float32)
Y = Y.astype(np.int32)

split = [ 70*n//100 ]
train_x, test_x = np.split(X, split)
train_labels, test_labels = np.split(Y, split)

def plot_dataset(features, labels, W=None, b=None):
    # prepare the plot
    fig, ax = plt.subplots(1, 1)
    ax.set_xlabel('$x_i[0]$ -- (feature 1)')
    ax.set_ylabel('$x_i[1]$ -- (feature 2)')
    colors = ['r' if l else 'b' for l in labels]
    ax.scatter(features[:, 0], features[:, 1], marker='o', c=colors, s=100, alpha = 0.5)
    if W is not None:
        min_x = min(features[:,0])
        max_x = max(features[:,1])
        min_y = min(features[:,1])*(1-.1)
        max_y = max(features[:,1])*(1+.1)
        cx = np.array([min_x,max_x],dtype=np.float32)
        cy = (0.5-W[0]*cx-b)/W[1]
        ax.plot(cx,cy,'g')
        ax.set_ylim(min_y,max_y)
    fig.show()

plot_dataset(train_x, train_labels)

train_x_norm = (train_x-np.min(train_x,axis=0)) / (np.max(train_x,axis=0)-np.min(train_x,axis=0))
test_x_norm = (test_x-np.min(train_x,axis=0)) / (np.max(train_x,axis=0)-np.min(train_x,axis=0))

model = keras.models.Sequential()
model.add(keras.Input(shape=(2,)))
model.add(keras.layers.Dense(1))
model.add(keras.layers.Activation(keras.activations.sigmoid))

model.summary()

model = keras.models.Sequential()
model.add(keras.layers.Dense(1,input_shape=(2,),activation='sigmoid'))
model.summary()

model.compile(optimizer=keras.optimizers.SGD(learning_rate=0.2),loss='binary_crossentropy',metrics=['acc'])

model.fit(x=train_x_norm,y=train_labels,validation_data=(test_x_norm,test_labels),epochs=10,batch_size=1)

plot_dataset(train_x,train_labels,model.layers[0].weights[0],model.layers[0].weights[1])

model = keras.models.Sequential([
    keras.layers.Dense(1,input_shape=(2,),activation='sigmoid')])
model.compile(optimizer=keras.optimizers.SGD(learning_rate=0.05),loss='binary_crossentropy',metrics=['acc'])
hist = model.fit(x=train_x_norm,y=train_labels,validation_data=(test_x_norm,test_labels),epochs=10,batch_size=1)

plt.plot(hist.history['acc'])
plt.plot(hist.history['val_acc'])

model = keras.models.Sequential([
    keras.layers.Dense(5,input_shape=(2,),activation='relu'),
    keras.layers.Dense(2,activation='softmax')
])
model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.01),
    loss="categorical_crossentropy",
    metrics=["accuracy"],
)

# Two ways to convert to one-hot encoding
train_labels_onehot = keras.utils.to_categorical(train_labels)
test_labels_onehot = np.eye(2)[test_labels]

hist = model.fit(x=train_x_norm,y=train_labels_onehot,
                 validation_data=[test_x_norm,test_labels_onehot],batch_size=1,epochs=10)

model.compile(
    optimizer=keras.optimizers.Adam(learning_rate=0.01),
    loss="sparse_categorical_crossentropy",
    metrics=["accuracy"],
)
model.fit(x=train_x_norm,y=train_labels,validation_data=[test_x_norm,test_labels],batch_size=1,epochs=10)