MulticlassClassifications.py

import numpy as np
import matplotlib.pyplot as plt
%matplotlib widget
from sklearn.datasets import make_blobs
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
np.set_printoptions(precision=2)
from lab_utils_multiclass_TF import *
import logging
logging.getLogger("tensorflow").setLevel(logging.ERROR)
tf.autograph.set_verbosity(0)

# make 4-class dataset for classification
classes = 4
m = 100
centers = [[-5, 2], [-2, -2], [1, 2], [5, -2]]
std = 1.0
X_train, y_train = make_blobs(n_samples=m, centers=centers, cluster_std=std,random_state=30)

plt_mc(X_train,y_train,classes, centers, std=std)

# show classes in data set
print(f"unique classes {np.unique(y_train)}")
# show how classes are represented
print(f"class representation {y_train[:10]}")
# show shapes of our dataset
print(f"shape of X_train: {X_train.shape}, shape of y_train: {y_train.shape}")

tf.random.set_seed(1234)  # applied to achieve consistent results
model = Sequential(
    [
        Dense(2, activation = 'relu',   name = "L1"),
        Dense(4, activation = 'linear', name = "L2")
    ]
)

'''
The statements below compile and train the network. Setting `from_logits=True` as an argument to the loss function 
specifies that the output activation was linear rather than a softmax.
'''

model.compile(
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=tf.keras.optimizers.Adam(0.01),
)

model.fit(
    X_train,y_train,
    epochs=200
)
# With the model trained, we can see how the model has classified the training data.
plt_cat_mc(X_train, y_train, model, classes)