coffe roasting

 

import numpy as np

import matplotlib.pyplot as plt

plt.style.use('./deeplearning.mplstyle')

import tensorflow as tf

from tensorflow.keras.models import Sequential

from tensorflow.keras.layers import Dense

from lab_utils_common import dlc

from lab_coffee_utils import load_coffee_data, plt_roast, plt_prob, plt_layer, plt_network, plt_output_unit

import logging

logging.getLogger("tensorflow").setLevel(logging.ERROR)

tf.autograph.set_verbosity(0)

X,Y = load_coffee_data();

print(X.shape, Y.shape)

plt_roast(X,Y)

print(f"Temperature Max, Min pre normalization: {np.max(X[:,0]):0.2f}, {np.min(X[:,0]):0.2f}")

print(f"Duration    Max, Min pre normalization: {np.max(X[:,1]):0.2f}, {np.min(X[:,1]):0.2f}")

norm_l = tf.keras.layers.Normalization(axis=-1)

norm_l.adapt(X)  # learns mean, variance

Xn = norm_l(X)

print(f"Temperature Max, Min post normalization: {np.max(Xn[:,0]):0.2f}, {np.min(Xn[:,0]):0.2f}")

print(f"Duration    Max, Min post normalization: {np.max(Xn[:,1]):0.2f}, {np.min(Xn[:,1]):0.2f}")

Xt = np.tile(Xn,(1000,1))

Yt= np.tile(Y,(1000,1))   

print(Xt.shape, Yt.shape)  

tf.random.set_seed(1234)  # applied to achieve consistent results

model = Sequential(

    [

        tf.keras.Input(shape=(2,)),

        Dense(3, activation='sigmoid', name = 'layer1'),

        Dense(1, activation='sigmoid', name = 'layer2')

     ]

)

model.summary()

L1_num_params = 2 * 3 + 3   # W1 parameters  + b1 parameters

L2_num_params = 3 * 1 + 1   # W2 parameters  + b2 parameters

print("L1 params = ", L1_num_params, ", L2 params = ", L2_num_params  )

W1, b1 = model.get_layer("layer1").get_weights()

W2, b2 = model.get_layer("layer2").get_weights()

print(f"W1{W1.shape}:\n", W1, f"\nb1{b1.shape}:", b1)

print(f"W2{W2.shape}:\n", W2, f"\nb2{b2.shape}:", b2)

model.compile(

    loss = tf.keras.losses.BinaryCrossentropy(),

    optimizer = tf.keras.optimizers.Adam(learning_rate=0.01),

)

model.fit(

    Xt,Yt,            

    epochs=10,

)

W1, b1 = model.get_layer("layer1").get_weights()

W2, b2 = model.get_layer("layer2").get_weights()

print("W1:\n", W1, "\nb1:", b1)

print("W2:\n", W2, "\nb2:", b2)

W1, b1 = model.get_layer("layer1").get_weights()

W2, b2 = model.get_layer("layer2").get_weights()

print("W1:\n", W1, "\nb1:", b1)

print("W2:\n", W2, "\nb2:", b2)

W1 = np.array([

    [-8.94,  0.29, 12.89],

    [-0.17, -7.34, 10.79]] )

b1 = np.array([-9.87, -9.28,  1.01])

W2 = np.array([

    [-31.38],

    [-27.86],

    [-32.79]])

b2 = np.array([15.54])

model.get_layer("layer1").set_weights([W1,b1])

model.get_layer("layer2").set_weights([W2,b2])

X_test = np.array([

    [200,13.9],  # postive example

    [200,17]])   # negative example

X_testn = norm_l(X_test)

predictions = model.predict(X_testn)

print("predictions = \n", predictions)

yhat = np.zeros_like(predictions)

for i in range(len(predictions)):

    if predictions[i] >= 0.5:

        yhat[i] = 1

    else:

        yhat[i] = 0

print(f"decisions = \n{yhat}")

plt_layer(X,Y.reshape(-1,),W1,b1,norm_l)

plt_output_unit(W2,b2)

netf= lambda x : model.predict(norm_l(x))

plt_network(X,Y,netf)