neurons and layers

 import numpy as np

import matplotlib.pyplot as plt

import tensorflow as tf

from tensorflow.keras.layers import Dense, Input

from tensorflow.keras import Sequential

from tensorflow.keras.losses import MeanSquaredError, BinaryCrossentropy

from tensorflow.keras.activations import sigmoid

from lab_utils_common import dlc

from lab_neurons_utils import plt_prob_1d, sigmoidnp, plt_linear, plt_logistic

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

import logging

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

tf.autograph.set_verbosity(0)

X_train = np.array([[1.0], [2.0]], dtype=np.float32)           #(size in 1000 square feet)

Y_train = np.array([[300.0], [500.0]], dtype=np.float32)       #(price in 1000s of dollars)

fig, ax = plt.subplots(1,1)

ax.scatter(X_train, Y_train, marker='x', c='r', label="Data Points")

ax.legend( fontsize='xx-large')

ax.set_ylabel('Price (in 1000s of dollars)', fontsize='xx-large')

ax.set_xlabel('Size (1000 sqft)', fontsize='xx-large')

plt.show()

linear_layer = tf.keras.layers.Dense(units=1, activation = 'linear', )

linear_layer.get_weights()

a1 = linear_layer(X_train[0].reshape(1,1))

print(a1)

w, b= linear_layer.get_weights()

print(f"w = {w}, b={b}")

set_w = np.array([[200]])

set_b = np.array([100])

# set_weights takes a list of numpy arrays

linear_layer.set_weights([set_w, set_b])

print(linear_layer.get_weights())

a1 = linear_layer(X_train[0].reshape(1,1))

print(a1)

alin = np.dot(set_w,X_train[0].reshape(1,1)) + set_b

print(alin)

prediction_tf = linear_layer(X_train)

prediction_np = np.dot( X_train, set_w) + set_b

plt_linear(X_train, Y_train, prediction_tf, prediction_np)

X_train = np.array([0., 1, 2, 3, 4, 5], dtype=np.float32).reshape(-1,1)  # 2-D Matrix

Y_train = np.array([0,  0, 0, 1, 1, 1], dtype=np.float32).reshape(-1,1)  # 2-D Matrix

pos = Y_train == 1

neg = Y_train == 0

X_train[pos]

pos = Y_train == 1

neg = Y_train == 0

fig,ax = plt.subplots(1,1,figsize=(4,3))

ax.scatter(X_train[pos], Y_train[pos], marker='x', s=80, c = 'red', label="y=1")

ax.scatter(X_train[neg], Y_train[neg], marker='o', s=100, label="y=0", facecolors='none', 

              edgecolors=dlc["dlblue"],lw=3)

ax.set_ylim(-0.08,1.1)

ax.set_ylabel('y', fontsize=12)

ax.set_xlabel('x', fontsize=12)

ax.set_title('one variable plot')

ax.legend(fontsize=12)

plt.show()

model = Sequential(

    [

        tf.keras.layers.Dense(1, input_dim=1,  activation = 'sigmoid', name='L1')

    ]

)

model.summary()

logistic_layer = model.get_layer('L1')

w,b = logistic_layer.get_weights()

print(w,b)

print(w.shape,b.shape)

set_w = np.array([[2]])

set_b = np.array([-4.5])

# set_weights takes a list of numpy arrays

logistic_layer.set_weights([set_w, set_b])

print(logistic_layer.get_weights())

a1 = model.predict(X_train[0].reshape(1,1))

print(a1)

alog = sigmoidnp(np.dot(set_w,X_train[0].reshape(1,1)) + set_b)

print(alog)

plt_logistic(X_train, Y_train, model, set_w, set_b, pos, neg)