nl_for_multiclass_classification.py

import numpy as np
import tensorflow as tf
from tensorflow.keras.models import Sequential
from tensorflow.keras.layers import Dense
from tensorflow.keras.activations import linear, relu, sigmoid
%matplotlib widget
import matplotlib.pyplot as plt
plt.style.use('./deeplearning.mplstyle')

import logging
logging.getLogger("tensorflow").setLevel(logging.ERROR)
tf.autograph.set_verbosity(0)

from public_tests import *

from autils import *
from lab_utils_softmax import plt_softmax
np.set_printoptions(precision=2)
plt_act_trio()
# UNQ_C1
# GRADED CELL: my_softmax

def my_softmax(z):
    """ Softmax converts a vector of values to a probability distribution.
    Args:
      z (ndarray (N,))  : input data, N features
    Returns:
      a (ndarray (N,))  : softmax of z
    """
    ### START CODE HERE ###
    ez = np.exp(z)              #element-wise exponenial
    a = ez/np.sum(ez)
    ### END CODE HERE ###
    return a

z = np.array([1., 2., 3., 4.])
a = my_softmax(z)
atf = tf.nn.softmax(z)
print(f"my_softmax(z):         {a}")
print(f"tensorflow softmax(z): {atf}")

# BEGIN UNIT TEST
test_my_softmax(my_softmax)
# END UNIT TEST

plt.close("all")
plt_softmax(my_softmax)

# load dataset
X, y = load_data()
print ('The first element of X is: ', X[0])
print ('The first element of y is: ', y[0,0])
print ('The last element of y is: ', y[-1,0])
print ('The shape of X is: ' + str(X.shape))
print ('The shape of y is: ' + str(y.shape))
import warnings

warnings.simplefilter(action='ignore', category=FutureWarning)
# You do not need to modify anything in this cell

m, n = X.shape

fig, axes = plt.subplots(8, 8, figsize=(5, 5))
fig.tight_layout(pad=0.13, rect=[0, 0.03, 1, 0.91])  # [left, bottom, right, top]

# fig.tight_layout(pad=0.5)
widgvis(fig)
for i, ax in enumerate(axes.flat):
    # Select random indices
    random_index = np.random.randint(m)

    # Select rows corresponding to the random indices and
    # reshape the image
    X_random_reshaped = X[random_index].reshape((20, 20)).T

    # Display the image
    ax.imshow(X_random_reshaped, cmap='gray')

    # Display the label above the image
    ax.set_title(y[random_index, 0])
    ax.set_axis_off()
    fig.suptitle("Label, image", fontsize=14)

# UNQ_C2
# GRADED CELL: Sequential model
tf.random.set_seed(1234) # for consistent results
model = Sequential(
    [
        ### START CODE HERE ###
        Dense(25, activation = 'relu',name = "L1"),
        Dense(15, activation = 'relu',name = "L2"),
        Dense(10, activation = 'linear',name = "L4"),
        ### END CODE HERE ###
    ], name = "my_model"
)
[layer1, layer2, layer3] = model.layers

#### Examine Weights shapes
W1,b1 = layer1.get_weights()
W2,b2 = layer2.get_weights()
W3,b3 = layer3.get_weights()
print(f"W1 shape = {W1.shape}, b1 shape = {b1.shape}")
print(f"W2 shape = {W2.shape}, b2 shape = {b2.shape}")
print(f"W3 shape = {W3.shape}, b3 shape = {b3.shape}")

model.compile(
    loss=tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True),
    optimizer=tf.keras.optimizers.Adam(learning_rate=0.001),
)

history = model.fit(
    X,y,
    epochs=40
)

plot_loss_tf(history)

image_of_two = X[1015]
display_digit(image_of_two)

prediction = model.predict(image_of_two.reshape(1,400))  # prediction

print(f" predicting a Two: \n{prediction}")
print(f" Largest Prediction index: {np.argmax(prediction)}")

prediction_p = tf.nn.softmax(prediction)

print(f" predicting a Two. Probability vector: \n{prediction_p}")
print(f"Total of predictions: {np.sum(prediction_p):0.3f}")
yhat = np.argmax(prediction_p)

print(f"np.argmax(prediction_p): {yhat}")

import warnings

warnings.simplefilter(action='ignore', category=FutureWarning)
# You do not need to modify anything in this cell

m, n = X.shape

fig, axes = plt.subplots(8, 8, figsize=(5, 5))
fig.tight_layout(pad=0.13, rect=[0, 0.03, 1, 0.91])  # [left, bottom, right, top]
widgvis(fig)
for i, ax in enumerate(axes.flat):
    # Select random indices
    random_index = np.random.randint(m)

    # Select rows corresponding to the random indices and
    # reshape the image
    X_random_reshaped = X[random_index].reshape((20, 20)).T

    # Display the image
    ax.imshow(X_random_reshaped, cmap='gray')

    # Predict using the Neural Network
    prediction = model.predict(X[random_index].reshape(1, 400))
    prediction_p = tf.nn.softmax(prediction)
    yhat = np.argmax(prediction_p)

    # Display the label above the image
    ax.set_title(f"{y[random_index, 0]},{yhat}", fontsize=10)
    ax.set_axis_off()
fig.suptitle("Label, yhat", fontsize=14)
plt.show()

print( f"{display_errors(model,X,y)} errors out of {len(X)} images")