
# Author: Stephen Situ
# This project aims to create a tensorflow neural network model for multiclass-classification of animals. Given the data in 3 csv files,
# we union them, and then join them with a class csv file for the full dataframe. We also note that if we use the category as integers,
# we must start with 0 for the neural network. Using our baseline model for multiclass-classification, we get an accuracy 28%. After we 
# make various changes like changing neurons per layer, epochs, learning rate, and optimizer. We find that increasing learning rate and
# neurons per layer were the most effective for our final model. This final model gets 100% accuracy. 
# Then we plot loss/accuracy charts for our models, create confusion matrices, and look at the model visualizations on tensorboard
# original data here: 
# https://www.kaggle.com/datasets/uciml/zoo-animal-classification
# https://www.kaggle.com/datasets/agajorte/zoo-animals-extended-dataset

C:\Users\Steve\anaconda3\lib\site-packages\scipy\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.1
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"


# Import Libraries
import numpy as np
import pandas as pd
import tensorflow as tf
import matplotlib.pyplot as plt

C:\Users\Steve\anaconda3\lib\site-packages\scipy\__init__.py:146: UserWarning: A NumPy version >=1.16.5 and <1.23.0 is required for this version of SciPy (detected version 1.24.1
  warnings.warn(f"A NumPy version >={np_minversion} and <{np_maxversion}"


# Read 3 csv files, union them, then rename column
# Then subtract 1 from "Class-Index" column to make classification intergers start from 0 (need to do this for neural network)
df1 = pd.read_csv("zoo.csv")
df2 = pd.read_csv("zoo2.csv")
df3 = pd.read_csv("zoo3.csv")
df4 = pd.concat([df1,df2,df3])
df5 = df4.rename(columns={'class_type': 'Class_Index'})
df5["Class_Index"] = df5["Class_Index"] - 1
df5


# Read class csv file and subtract 1 from "Class_number"
df_class = pd.read_csv("class.csv")
df_class["Class_Index"] = df_class["Class_Number"] - 1


df_class


# Join dataframe to class dataframe on 'Class_Index'
# then drop unneeded columns
df6 = pd.merge(df5, df_class, on='Class_Index', how='outer')
df7 = df6.drop(columns=['animal_name', 'Number_Of_Animal_Species_In_Class', 'Class_Type', 'Animal_Names','Class_Number'])


# view final dataframe
df7


# Check data types
df7.dtypes

hair           int64
feathers       int64
eggs           int64
milk           int64
airborne       int64
aquatic        int64
predator       int64
toothed        int64
backbone       int64
breathes       int64
venomous       int64
fins           int64
legs           int64
tail           int64
domestic       int64
catsize        int64
Class_Index    int64
dtype: object


# Split data into train & test set along with x & y
from sklearn.model_selection import train_test_split
train_data, test_data = train_test_split(df7, test_size=0.2)
train_data_y = train_data["Class_Index"]
train_data_x = train_data.drop(columns=["Class_Index"])
test_data_y = test_data["Class_Index"]
test_data_x = test_data.drop(columns=["Class_Index"])


# Convert pandas dataframes into numpy arrays
train_data_y_ten = train_data_y.values
train_data_x_ten = train_data_x.values
test_data_y_ten = test_data_y.values
test_data_x_ten = test_data_x.values


# Check how arrays look and their shape
print(train_data_y_ten,train_data_x_ten,test_data_y_ten,test_data_x_ten)
print(train_data_y_ten.shape,train_data_x_ten.shape,test_data_y_ten.shape,test_data_x_ten.shape)

[0 4 2 3 1 1 3 1 6 3 0 3 5 0 0 0 6 0 1 0 5 1 3 6 6 4 2 1 5 2 3 0 4 1 0 6 0
 1 1 0 0 6 2 5 3 3 0 3 2 1 0 0 1 5 1 4 5 0 0 0 0 3 5 0 4 0 1 3 0 6 6 1 0 1
 1 6 5 1 0 1 0 3 1 0 0 4 5 0 1 5 3 0 5 6 5 0 4 0 4 1 4 1 0 0 3 0 1 2 1 3 2
 2 2 1 3 0 5 3 4 2 2 4 5 0 3 0 1 2 2 6 2 1 1 5 1 0 1 4 4 2 4 0 1 0 3 5 0 0
 0 0 0 5 4 2 3 1 0 0 1 4 0 1 0 6 6 3 2 0 0 3 0] [[1 0 0 ... 1 0 1]
 [0 0 1 ... 0 0 0]
 [0 0 1 ... 1 1 0]
 ...
 [1 0 0 ... 1 0 1]
 [0 0 1 ... 1 0 1]
 [1 0 0 ... 1 0 1]] [6 1 1 5 5 0 1 5 3 0 0 0 6 6 5 1 2 2 3 0 6 6 3 3 0 6 2 5 2 3 1 2 2 1 0 6 5
 0 2 5 3 2 5] [[0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0]
 [0 1 1 0 1 0 1 0 1 1 0 0 2 1 0 1]
 [0 1 1 0 1 1 0 0 1 1 0 0 2 1 0 0]
 [0 0 1 0 0 0 0 0 0 1 0 0 6 0 0 0]
 [0 0 1 0 0 0 0 0 0 1 0 0 6 0 0 0]
 [1 0 0 1 0 1 1 1 1 1 0 1 4 1 0 0]
 [0 1 1 0 1 0 0 0 1 1 0 0 2 1 0 0]
 [0 0 1 0 1 0 1 0 0 1 1 0 6 0 0 0]
 [0 0 1 0 0 1 0 1 1 0 0 1 0 1 0 0]
 [1 0 0 1 0 0 0 1 1 1 0 0 4 0 1 0]
 [1 0 1 1 0 1 1 0 1 1 0 0 4 1 0 1]
 [1 0 0 1 0 0 0 1 1 1 0 0 4 1 0 0]
 [0 0 1 0 0 1 1 0 0 0 0 0 8 0 0 1]
 [0 0 1 0 0 1 0 0 0 0 0 0 0 0 0 1]
 [0 0 1 0 0 0 0 0 0 1 0 0 6 0 0 0]
 [0 1 1 0 1 1 1 0 1 1 0 0 2 1 0 0]
 [0 0 1 0 0 0 1 1 1 1 0 0 0 1 0 1]
 [0 0 1 0 0 1 1 1 1 1 0 0 4 1 0 0]
 [0 0 1 0 0 1 1 1 1 0 0 1 0 1 0 0]
 [1 0 0 1 0 0 0 0 1 1 0 0 4 1 0 0]
 [0 0 1 0 0 1 0 0 0 0 1 0 0 0 0 1]
 [0 0 1 0 0 0 0 0 0 1 0 0 0 0 0 0]
 [0 0 1 0 0 1 0 1 1 0 0 1 0 1 1 0]
 [0 0 1 0 0 1 1 1 1 0 0 1 0 1 0 1]
 [1 0 0 1 0 0 0 1 1 1 0 0 4 1 0 1]
 [0 0 1 0 0 0 1 1 0 1 1 0 8 0 0 0]
 [0 0 0 0 0 1 1 1 1 0 1 0 0 1 0 0]
 [0 0 1 0 1 0 0 0 0 1 0 0 6 0 0 0]
 [0 0 1 0 0 1 0 0 1 1 0 0 4 0 1 1]
 [0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 0]
 [0 1 1 0 0 0 1 0 1 1 0 0 2 1 0 0]
 [0 0 1 0 0 1 1 1 1 1 0 0 4 1 0 1]
 [0 0 1 0 0 0 0 1 1 1 0 0 4 1 0 0]
 [0 1 1 0 1 0 1 0 1 1 0 0 2 1 0 1]
 [0 0 0 1 0 1 1 1 1 1 0 1 0 1 0 1]
 [0 0 1 0 0 1 1 0 0 0 0 0 6 0 0 0]
 [0 0 1 0 1 0 0 0 0 1 0 0 6 0 0 0]
 [1 0 0 1 0 0 0 1 1 1 0 0 4 1 1 1]
 [0 0 1 0 0 0 1 1 1 1 1 0 0 1 0 1]
 [0 0 1 0 1 0 0 0 0 1 0 0 6 0 0 0]
 [0 0 1 0 0 1 0 0 1 0 0 1 0 1 0 1]
 [0 0 1 0 0 0 1 1 1 1 0 0 0 1 0 0]
 [1 0 1 0 1 0 0 0 0 1 0 0 6 0 0 0]]
(171,) (171, 16) (43,) (43, 16)


# For Multi-Class classification with 7 categories, set last dense layer to 7 and activation function to "softmax"
# Also use SparseCategoricalCrossentropy() for loss function for integers
# Use Categorical Cross Entropy if data is one hot encoded
# default lr is 0.001
# start with 20 epochs 2 hidden layers with 4 neurons


# Add callback for tensorboard
from tensorflow.keras.callbacks import TensorBoard
file_name = "my_saved_model"
tensorboard = TensorBoard(log_dir="logs\\{}".format(file_name))


# Create tensorflow multiclass-classification model
# Get 26% accuracy on first model
# Set random seed
tf.random.set_seed(45)

# Create the model
model_1 = tf.keras.Sequential([
  tf.keras.layers.Dense(4, activation="relu"),
  tf.keras.layers.Dense(4, activation="relu"),
  tf.keras.layers.Dense(7, activation="softmax") 
])

# Compile the model
model_1.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),
                 optimizer=tf.keras.optimizers.Adam(lr=0.001), 
                 metrics=["accuracy"])

# Fit the model
history_1 = model_1.fit(test_data_x_ten,
                       test_data_y_ten,
                       epochs=20)

Epoch 1/20
2/2 [==============================] - 0s 2ms/step - loss: 1.9404 - accuracy: 0.3256
Epoch 2/20
2/2 [==============================] - 0s 2ms/step - loss: 1.9374 - accuracy: 0.2326
Epoch 3/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9349 - accuracy: 0.3256
Epoch 4/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9321 - accuracy: 0.3256
Epoch 5/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9297 - accuracy: 0.3256
Epoch 6/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9271 - accuracy: 0.3256
Epoch 7/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9247 - accuracy: 0.3256
Epoch 8/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9223 - accuracy: 0.3256
Epoch 9/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9204 - accuracy: 0.3023
Epoch 10/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9184 - accuracy: 0.2558
Epoch 11/20
2/2 [==============================] - 0s 5ms/step - loss: 1.9164 - accuracy: 0.2558
Epoch 12/20
2/2 [==============================] - 0s 2ms/step - loss: 1.9144 - accuracy: 0.2558
Epoch 13/20
2/2 [==============================] - 0s 5ms/step - loss: 1.9123 - accuracy: 0.2558
Epoch 14/20
2/2 [==============================] - 0s 2ms/step - loss: 1.9106 - accuracy: 0.2558
Epoch 15/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9085 - accuracy: 0.2558
Epoch 16/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9064 - accuracy: 0.2558
Epoch 17/20
2/2 [==============================] - 0s 5ms/step - loss: 1.9047 - accuracy: 0.2558
Epoch 18/20
2/2 [==============================] - 0s 2ms/step - loss: 1.9027 - accuracy: 0.2558
Epoch 19/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9010 - accuracy: 0.2558
Epoch 20/20
2/2 [==============================] - 0s 4ms/step - loss: 1.8992 - accuracy: 0.2558


# Create tensorflow multiclass-classification model
# Try using 2 100 neuron layers and get 88% accuracy
# Set random seed
tf.random.set_seed(45)

# Create the model
model_2 = tf.keras.Sequential([
  tf.keras.layers.Dense(100, activation="relu"),
  tf.keras.layers.Dense(100, activation="relu"),
  tf.keras.layers.Dense(7, activation="softmax") 
])

# Compile the model
model_2.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),
                 optimizer=tf.keras.optimizers.Adam(lr=0.001), 
                 metrics=["accuracy"])

# Fit the model
history_2 = model_2.fit(test_data_x_ten,
                       test_data_y_ten,
                       epochs=20)

Epoch 1/20
2/2 [==============================] - 0s 3ms/step - loss: 2.0811 - accuracy: 0.1860
Epoch 2/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9291 - accuracy: 0.3256
Epoch 3/20
2/2 [==============================] - 0s 3ms/step - loss: 1.7984 - accuracy: 0.4651
Epoch 4/20
2/2 [==============================] - 0s 4ms/step - loss: 1.6970 - accuracy: 0.5116
Epoch 5/20
2/2 [==============================] - 0s 3ms/step - loss: 1.6163 - accuracy: 0.5581
Epoch 6/20
2/2 [==============================] - 0s 3ms/step - loss: 1.5426 - accuracy: 0.7674
Epoch 7/20
2/2 [==============================] - 0s 3ms/step - loss: 1.4746 - accuracy: 0.7674
Epoch 8/20
2/2 [==============================] - 0s 4ms/step - loss: 1.4173 - accuracy: 0.7674
Epoch 9/20
2/2 [==============================] - 0s 5ms/step - loss: 1.3626 - accuracy: 0.7674
Epoch 10/20
2/2 [==============================] - 0s 4ms/step - loss: 1.3106 - accuracy: 0.7674
Epoch 11/20
2/2 [==============================] - 0s 4ms/step - loss: 1.2581 - accuracy: 0.8372
Epoch 12/20
2/2 [==============================] - 0s 3ms/step - loss: 1.2030 - accuracy: 0.8372
Epoch 13/20
2/2 [==============================] - 0s 4ms/step - loss: 1.1503 - accuracy: 0.8372
Epoch 14/20
2/2 [==============================] - 0s 4ms/step - loss: 1.0952 - accuracy: 0.8605
Epoch 15/20
2/2 [==============================] - 0s 4ms/step - loss: 1.0418 - accuracy: 0.8837
Epoch 16/20
2/2 [==============================] - 0s 4ms/step - loss: 0.9876 - accuracy: 0.8837
Epoch 17/20
2/2 [==============================] - 0s 4ms/step - loss: 0.9382 - accuracy: 0.8837
Epoch 18/20
2/2 [==============================] - 0s 3ms/step - loss: 0.8886 - accuracy: 0.8837
Epoch 19/20
2/2 [==============================] - 0s 3ms/step - loss: 0.8442 - accuracy: 0.8837
Epoch 20/20
2/2 [==============================] - 0s 3ms/step - loss: 0.7980 - accuracy: 0.8837


# Create tensorflow multiclass-classification model
# try doubling the epochs, epochs = 40
# Get 26% accuracy
# Set random seed
tf.random.set_seed(45)

# Create the model
model_3 = tf.keras.Sequential([
  tf.keras.layers.Dense(4, activation="relu"),
  tf.keras.layers.Dense(4, activation="relu"),
  tf.keras.layers.Dense(7, activation="softmax")
])

# Compile the model
model_3.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),
                 optimizer=tf.keras.optimizers.Adam(lr=0.001), 
                 metrics=["accuracy"])

# Fit the model
history_3 = model_3.fit(test_data_x_ten,
                       test_data_y_ten,
                       epochs=40)

Epoch 1/40
2/2 [==============================] - 0s 5ms/step - loss: 1.9404 - accuracy: 0.3256
Epoch 2/40
2/2 [==============================] - 0s 6ms/step - loss: 1.9374 - accuracy: 0.2326
Epoch 3/40
2/2 [==============================] - 0s 4ms/step - loss: 1.9349 - accuracy: 0.3256
Epoch 4/40
2/2 [==============================] - 0s 6ms/step - loss: 1.9321 - accuracy: 0.3256
Epoch 5/40
2/2 [==============================] - 0s 5ms/step - loss: 1.9297 - accuracy: 0.3256
Epoch 6/40
2/2 [==============================] - 0s 9ms/step - loss: 1.9271 - accuracy: 0.3256
Epoch 7/40
2/2 [==============================] - 0s 3ms/step - loss: 1.9247 - accuracy: 0.3256
Epoch 8/40
2/2 [==============================] - 0s 3ms/step - loss: 1.9223 - accuracy: 0.3256
Epoch 9/40
2/2 [==============================] - 0s 4ms/step - loss: 1.9204 - accuracy: 0.3023
Epoch 10/40
2/2 [==============================] - 0s 5ms/step - loss: 1.9184 - accuracy: 0.2558
Epoch 11/40
2/2 [==============================] - 0s 6ms/step - loss: 1.9164 - accuracy: 0.2558
Epoch 12/40
2/2 [==============================] - 0s 7ms/step - loss: 1.9144 - accuracy: 0.2558
Epoch 13/40
2/2 [==============================] - 0s 5ms/step - loss: 1.9123 - accuracy: 0.2558
Epoch 14/40
2/2 [==============================] - 0s 4ms/step - loss: 1.9106 - accuracy: 0.2558
Epoch 15/40
2/2 [==============================] - 0s 6ms/step - loss: 1.9085 - accuracy: 0.2558
Epoch 16/40
2/2 [==============================] - 0s 4ms/step - loss: 1.9064 - accuracy: 0.2558
Epoch 17/40
2/2 [==============================] - 0s 6ms/step - loss: 1.9047 - accuracy: 0.2558
Epoch 18/40
2/2 [==============================] - 0s 5ms/step - loss: 1.9027 - accuracy: 0.2558
Epoch 19/40
2/2 [==============================] - 0s 7ms/step - loss: 1.9010 - accuracy: 0.2558
Epoch 20/40
2/2 [==============================] - 0s 7ms/step - loss: 1.8992 - accuracy: 0.2558
Epoch 21/40
2/2 [==============================] - 0s 5ms/step - loss: 1.8974 - accuracy: 0.2558
Epoch 22/40
2/2 [==============================] - 0s 5ms/step - loss: 1.8958 - accuracy: 0.2558
Epoch 23/40
2/2 [==============================] - 0s 6ms/step - loss: 1.8941 - accuracy: 0.2558
Epoch 24/40
2/2 [==============================] - 0s 6ms/step - loss: 1.8924 - accuracy: 0.2558
Epoch 25/40
2/2 [==============================] - 0s 3ms/step - loss: 1.8909 - accuracy: 0.2558
Epoch 26/40
2/2 [==============================] - 0s 4ms/step - loss: 1.8892 - accuracy: 0.2558
Epoch 27/40
2/2 [==============================] - 0s 4ms/step - loss: 1.8876 - accuracy: 0.2558
Epoch 28/40
2/2 [==============================] - 0s 6ms/step - loss: 1.8860 - accuracy: 0.2558
Epoch 29/40
2/2 [==============================] - 0s 7ms/step - loss: 1.8844 - accuracy: 0.2558
Epoch 30/40
2/2 [==============================] - 0s 5ms/step - loss: 1.8826 - accuracy: 0.2558
Epoch 31/40
2/2 [==============================] - 0s 4ms/step - loss: 1.8808 - accuracy: 0.2558
Epoch 32/40
2/2 [==============================] - 0s 3ms/step - loss: 1.8794 - accuracy: 0.2558
Epoch 33/40
2/2 [==============================] - 0s 4ms/step - loss: 1.8776 - accuracy: 0.2558
Epoch 34/40
2/2 [==============================] - 0s 3ms/step - loss: 1.8759 - accuracy: 0.2558
Epoch 35/40
2/2 [==============================] - 0s 5ms/step - loss: 1.8742 - accuracy: 0.2558
Epoch 36/40
2/2 [==============================] - 0s 4ms/step - loss: 1.8724 - accuracy: 0.2558
Epoch 37/40
2/2 [==============================] - 0s 4ms/step - loss: 1.8709 - accuracy: 0.2558
Epoch 38/40
2/2 [==============================] - 0s 5ms/step - loss: 1.8690 - accuracy: 0.2558
Epoch 39/40
2/2 [==============================] - 0s 3ms/step - loss: 1.8671 - accuracy: 0.2558
Epoch 40/40
2/2 [==============================] - 0s 6ms/step - loss: 1.8654 - accuracy: 0.2558


# Create tensorflow multiclass-classification model
# Try learning rate of 0.01 and get 30% accuracy
# Set random seed
tf.random.set_seed(45)

# Create the model
model_4 = tf.keras.Sequential([
  tf.keras.layers.Dense(4, activation="relu"),
  tf.keras.layers.Dense(4, activation="relu"),
  tf.keras.layers.Dense(7, activation="softmax")
])

# Compile the model
model_4.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),
                 optimizer=tf.keras.optimizers.Adam(lr=0.01), 
                 metrics=["accuracy"])

# Fit the model
history_4 = model_4.fit(test_data_x_ten,
                       test_data_y_ten,
                       epochs=20)

Epoch 1/20
2/2 [==============================] - 0s 2ms/step - loss: 1.9368 - accuracy: 0.3256
Epoch 2/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9089 - accuracy: 0.2326
Epoch 3/20
2/2 [==============================] - 0s 2ms/step - loss: 1.8906 - accuracy: 0.3256
Epoch 4/20
2/2 [==============================] - 0s 3ms/step - loss: 1.8720 - accuracy: 0.3256
Epoch 5/20
2/2 [==============================] - 0s 4ms/step - loss: 1.8585 - accuracy: 0.3256
Epoch 6/20
2/2 [==============================] - 0s 3ms/step - loss: 1.8421 - accuracy: 0.3256
Epoch 7/20
2/2 [==============================] - 0s 3ms/step - loss: 1.8234 - accuracy: 0.3256
Epoch 8/20
2/2 [==============================] - 0s 4ms/step - loss: 1.8052 - accuracy: 0.3256
Epoch 9/20
2/2 [==============================] - 0s 4ms/step - loss: 1.7887 - accuracy: 0.3023
Epoch 10/20
2/2 [==============================] - 0s 4ms/step - loss: 1.7675 - accuracy: 0.2558
Epoch 11/20
2/2 [==============================] - 0s 4ms/step - loss: 1.7474 - accuracy: 0.2558
Epoch 12/20
2/2 [==============================] - 0s 3ms/step - loss: 1.7255 - accuracy: 0.2558
Epoch 13/20
2/2 [==============================] - 0s 3ms/step - loss: 1.7055 - accuracy: 0.2558
Epoch 14/20
2/2 [==============================] - 0s 3ms/step - loss: 1.6830 - accuracy: 0.2558
Epoch 15/20
2/2 [==============================] - 0s 5ms/step - loss: 1.6638 - accuracy: 0.2558
Epoch 16/20
2/2 [==============================] - 0s 4ms/step - loss: 1.6416 - accuracy: 0.2791
Epoch 17/20
2/2 [==============================] - 0s 3ms/step - loss: 1.6245 - accuracy: 0.3023
Epoch 18/20
2/2 [==============================] - 0s 3ms/step - loss: 1.6073 - accuracy: 0.3023
Epoch 19/20
2/2 [==============================] - 0s 3ms/step - loss: 1.5899 - accuracy: 0.3023
Epoch 20/20
2/2 [==============================] - 0s 3ms/step - loss: 1.5716 - accuracy: 0.3023


# Create tensorflow multiclass-classification model
# Try using Stochastic Gradient Descent optimizer, get 26%
# Set random seed
tf.random.set_seed(45)

# Create the model
model_5 = tf.keras.Sequential([
  tf.keras.layers.Dense(4, activation="relu"),
  tf.keras.layers.Dense(4, activation="relu"),
  tf.keras.layers.Dense(7, activation="softmax") 
])

# Compile the model
model_5.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),
                 optimizer=tf.keras.optimizers.SGD(lr=0.001),
                 metrics=["accuracy"])

# Fit the model
history_5 = model_5.fit(test_data_x_ten,
                       test_data_y_ten,
                       epochs=20)

Epoch 1/20

C:\Users\Steve\anaconda3\lib\site-packages\keras\optimizers\optimizer_v2\gradient_descent.py:108: UserWarning: The `lr` argument is deprecated, use `learning_rate` instead.
  super(SGD, self).__init__(name, **kwargs)

2/2 [==============================] - 0s 2ms/step - loss: 1.9408 - accuracy: 0.3023
Epoch 2/20
2/2 [==============================] - 0s 5ms/step - loss: 1.9406 - accuracy: 0.3488
Epoch 3/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9404 - accuracy: 0.2558
Epoch 4/20
2/2 [==============================] - 0s 2ms/step - loss: 1.9403 - accuracy: 0.2326
Epoch 5/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9401 - accuracy: 0.2558
Epoch 6/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9399 - accuracy: 0.2558
Epoch 7/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9397 - accuracy: 0.2558
Epoch 8/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9396 - accuracy: 0.2558
Epoch 9/20
2/2 [==============================] - 0s 17ms/step - loss: 1.9394 - accuracy: 0.2558
Epoch 10/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9392 - accuracy: 0.2558
Epoch 11/20
2/2 [==============================] - 0s 2ms/step - loss: 1.9390 - accuracy: 0.2558
Epoch 12/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9389 - accuracy: 0.2558
Epoch 13/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9387 - accuracy: 0.2558
Epoch 14/20
2/2 [==============================] - 0s 2ms/step - loss: 1.9385 - accuracy: 0.2558
Epoch 15/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9383 - accuracy: 0.2558
Epoch 16/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9381 - accuracy: 0.2558
Epoch 17/20
2/2 [==============================] - 0s 5ms/step - loss: 1.9379 - accuracy: 0.2558
Epoch 18/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9378 - accuracy: 0.2558
Epoch 19/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9376 - accuracy: 0.2558
Epoch 20/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9374 - accuracy: 0.2558


# Create tensorflow multiclass-classification model
# Try using learning rate callback to find optimal learning rate
# Set random seed
tf.random.set_seed(45)

# Create the model
model_6 = tf.keras.Sequential([
  tf.keras.layers.Dense(4, activation="relu"),
  tf.keras.layers.Dense(4, activation="relu"),
  tf.keras.layers.Dense(7, activation="softmax") 
])

# Compile the model
model_6.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),
                 optimizer=tf.keras.optimizers.Adam(lr=0.001), # ideal learning rate (same as default)
                 metrics=["accuracy"])

# Create a learning rate scheduler callback
lr_scheduler = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 1e-4 * 10**(epoch/20)) # traverse a set of learning rate values starting from 1e-4, increasing by 10**(epoch/20) every epoch


# Fit the model
history_6 = model_6.fit(test_data_x_ten,
                       test_data_y_ten,
                       epochs=20,
                      callbacks=[lr_scheduler])

Epoch 1/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9408 - accuracy: 0.3256 - lr: 1.0000e-04
Epoch 2/20
2/2 [==============================] - 0s 6ms/step - loss: 1.9405 - accuracy: 0.3256 - lr: 1.1220e-04
Epoch 3/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9402 - accuracy: 0.3256 - lr: 1.2589e-04
Epoch 4/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9398 - accuracy: 0.3256 - lr: 1.4125e-04
Epoch 5/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9395 - accuracy: 0.3256 - lr: 1.5849e-04
Epoch 6/20
2/2 [==============================] - 0s 5ms/step - loss: 1.9390 - accuracy: 0.3256 - lr: 1.7783e-04
Epoch 7/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9386 - accuracy: 0.3256 - lr: 1.9953e-04
Epoch 8/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9381 - accuracy: 0.3256 - lr: 2.2387e-04
Epoch 9/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9375 - accuracy: 0.2558 - lr: 2.5119e-04
Epoch 10/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9369 - accuracy: 0.2558 - lr: 2.8184e-04
Epoch 11/20
2/2 [==============================] - 0s 5ms/step - loss: 1.9363 - accuracy: 0.2558 - lr: 3.1623e-04
Epoch 12/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9355 - accuracy: 0.2558 - lr: 3.5481e-04
Epoch 13/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9345 - accuracy: 0.2558 - lr: 3.9811e-04
Epoch 14/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9336 - accuracy: 0.2558 - lr: 4.4668e-04
Epoch 15/20
2/2 [==============================] - 0s 5ms/step - loss: 1.9325 - accuracy: 0.2558 - lr: 5.0119e-04
Epoch 16/20
2/2 [==============================] - 0s 3ms/step - loss: 1.9312 - accuracy: 0.2558 - lr: 5.6234e-04
Epoch 17/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9298 - accuracy: 0.2558 - lr: 6.3096e-04
Epoch 18/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9283 - accuracy: 0.2558 - lr: 7.0795e-04
Epoch 19/20
2/2 [==============================] - 0s 5ms/step - loss: 1.9264 - accuracy: 0.2558 - lr: 7.9433e-04
Epoch 20/20
2/2 [==============================] - 0s 4ms/step - loss: 1.9245 - accuracy: 0.2558 - lr: 8.9125e-04


# Best model results were when we used 2 100 neurons layers and also increasing lr from 0.001 to 0.01
# We will try a combination
# Create tensorflow multiclass-classification model
# Try using 2 100 neuron layers with lr = 0.01, we get  accuracy close to 100% accuracy
# Set random seed
tf.random.set_seed(45)

# Create the model
model_7 = tf.keras.Sequential([
  tf.keras.layers.Dense(100, activation="relu"),
  tf.keras.layers.Dense(100, activation="relu"),
  tf.keras.layers.Dense(7, activation="softmax") # output shape is 39, activation is softmax
])

# Compile the model
model_7.compile(loss=tf.keras.losses.SparseCategoricalCrossentropy(),
                 optimizer=tf.keras.optimizers.Adam(lr=0.01), # ideal learning rate (same as default)
                 metrics=["accuracy"])

# Fit the model
history_7 = model_7.fit(test_data_x_ten,
                       test_data_y_ten,
                       epochs=20, callbacks=[tensorboard])

Epoch 1/20
2/2 [==============================] - 0s 13ms/step - loss: 1.9478 - accuracy: 0.3023
Epoch 2/20
2/2 [==============================] - 0s 11ms/step - loss: 1.3667 - accuracy: 0.4186
Epoch 3/20
2/2 [==============================] - 0s 13ms/step - loss: 0.9857 - accuracy: 0.7209
Epoch 4/20
2/2 [==============================] - 0s 13ms/step - loss: 0.6798 - accuracy: 0.8605
Epoch 5/20
2/2 [==============================] - 0s 10ms/step - loss: 0.4706 - accuracy: 0.9070
Epoch 6/20
2/2 [==============================] - 0s 13ms/step - loss: 0.2939 - accuracy: 0.9767
Epoch 7/20
2/2 [==============================] - 0s 11ms/step - loss: 0.1898 - accuracy: 0.9767
Epoch 8/20
2/2 [==============================] - 0s 10ms/step - loss: 0.1183 - accuracy: 1.0000
Epoch 9/20
2/2 [==============================] - 0s 13ms/step - loss: 0.0689 - accuracy: 1.0000
Epoch 10/20
2/2 [==============================] - 0s 12ms/step - loss: 0.0514 - accuracy: 0.9767
Epoch 11/20
2/2 [==============================] - 0s 14ms/step - loss: 0.0236 - accuracy: 1.0000
Epoch 12/20
2/2 [==============================] - 0s 12ms/step - loss: 0.0478 - accuracy: 0.9767
Epoch 13/20
2/2 [==============================] - 0s 9ms/step - loss: 0.0158 - accuracy: 1.0000
Epoch 14/20
2/2 [==============================] - 0s 12ms/step - loss: 0.0170 - accuracy: 1.0000
Epoch 15/20
2/2 [==============================] - 0s 10ms/step - loss: 0.0357 - accuracy: 0.9767
Epoch 16/20
2/2 [==============================] - 0s 10ms/step - loss: 0.0111 - accuracy: 1.0000
Epoch 17/20
2/2 [==============================] - 0s 11ms/step - loss: 0.0035 - accuracy: 1.0000
Epoch 18/20
2/2 [==============================] - 0s 9ms/step - loss: 0.0027 - accuracy: 1.0000
Epoch 19/20
2/2 [==============================] - 0s 12ms/step - loss: 0.0034 - accuracy: 1.0000
Epoch 20/20
2/2 [==============================] - 0s 11ms/step - loss: 0.0041 - accuracy: 1.0000


# 1. Increasing neuron density on hidden layers helped the model
# 2. Doubling the number of epochs did not help
# 3. Increasing the learning rate from 0.001 to 0.01 helped
# 4. Switching optimizer from adams to SGD did not help


# Plot the loss curves using .history method
pd.DataFrame(history_1.history).plot()
plt.title("model_1 loss/accuracy chart")
plt.show()
pd.DataFrame(history_2.history).plot()
plt.title("model_2 loss/accuracy chart")
plt.show()
pd.DataFrame(history_3.history).plot()
plt.title("model_3 loss/accuracy chart")
plt.show()
pd.DataFrame(history_4.history).plot()
plt.title("model_4 loss/accuracy chart")
plt.show()
pd.DataFrame(history_5.history).plot()
plt.title("model_5 loss/accuracy chart")
plt.show()
pd.DataFrame(history_7.history).plot()
plt.title("model_7 loss/accuracy chart")
plt.show()


# Checkout the history for model_6
pd.DataFrame(history_6.history).plot(figsize=(10,7), xlabel="epochs");


# Plot the learning rate versus the loss
# Rule of thumb, optimal learning rate is 10x smaller than bottom of the curve
# In this case, bottom is 0.1, 10x smaller is 0.01
lrs = np.linspace(start=1e-4, stop=1e-1, num=20)
plt.figure(figsize=(10, 7))
plt.semilogx(lrs, history_6.history["loss"]) # we want the x-axis (learning rate) to be log scale
plt.xlabel("Learning Rate")
plt.ylabel("Loss")
plt.title("Learning rate vs. loss");


# We will use model 7 to make evaluation due to having the best accuracy
# Check the accuracy of our model
loss, accuracy = model_7.evaluate(test_data_x_ten, test_data_y_ten )
print(f"Model loss on test set: {loss}")
print(f"Model accuracy on test set: {(accuracy*100):.2f}%")

2/2 [==============================] - 0s 3ms/step - loss: 0.0026 - accuracy: 1.0000
Model loss on test set: 0.0025535537861287594
Model accuracy on test set: 100.00%


# Get actual output of predictions
y_preds_ten = model_7.predict(test_data_x_ten)

2/2 [==============================] - 0s 2ms/step


# Check shape
y_preds_ten.shape

(43, 7)


# Check output numpy array
for i in y_preds_ten:
    print(y_preds_ten)

[[4.68069672e-08 9.99866247e-01 1.50920289e-06 9.34248590e-09
  1.31981200e-04 2.53384286e-07 3.34231807e-08]
 [2.36613484e-08 9.99964297e-01 2.40244418e-07 2.35388553e-09
  3.54334370e-05 8.92790819e-09 1.06643905e-09]
 [9.99983847e-01 2.37557707e-09 1.60234995e-05 1.45514101e-10
  9.02075996e-08 1.25780733e-10 2.95655167e-11]
 [9.99960244e-01 2.84082646e-10 3.97081276e-05 1.26955876e-10
  4.27145253e-08 1.00732707e-10 6.33287936e-11]
 [7.14975812e-09 8.86746232e-11 7.69566313e-08 9.99998271e-01
  8.96457486e-09 1.58959204e-13 1.57999750e-06]
 [1.55074318e-08 3.80016886e-11 1.63797381e-06 9.99994695e-01
  1.24140271e-08 3.48439458e-13 3.60273634e-06]
 [9.99997556e-01 2.89486723e-09 2.22689641e-06 1.44268611e-10
  7.86890482e-08 1.94717124e-11 7.16255917e-12]
 [5.49069306e-08 9.98675585e-01 7.06272403e-05 2.73462933e-06
  1.24586874e-03 1.89487182e-07 4.94792675e-06]
 [4.79785953e-08 4.34143260e-07 2.14042212e-03 1.50606311e-07
  1.23158388e-03 1.15208188e-02 9.85106528e-01]
 [9.99467147e-07 9.97832000e-01 1.06061398e-05 3.08742955e-07
  2.15175049e-03 3.44625300e-06 9.57089469e-07]
 [2.80600898e-06 9.99102354e-01 3.87644395e-05 3.34948197e-07
  8.55164835e-04 4.38081372e-07 1.22628421e-07]
 [3.87332315e-04 1.54138036e-07 9.98058140e-01 4.94299631e-04
  1.35860400e-05 3.14124463e-07 1.04606303e-03]
 [9.44275946e-09 3.79351803e-08 2.63843045e-08 9.99996364e-01
  2.17737181e-07 5.36950822e-12 3.27758266e-06]
 [2.23161396e-06 9.87678170e-01 8.99316001e-05 1.08850350e-06
  1.20543074e-02 1.39722644e-04 3.44977670e-05]
 [6.82858808e-05 1.20739728e-07 9.99574482e-01 3.66157042e-06
  3.05989641e-04 8.12568317e-07 4.66394522e-05]
 [5.42386202e-04 1.91148297e-06 9.98673320e-01 1.48296185e-05
  7.53004686e-04 3.18723323e-07 1.42170047e-05]
 [2.36613484e-08 9.99964297e-01 2.40244418e-07 2.35388553e-09
  3.54334370e-05 8.92790819e-09 1.06643905e-09]
 [2.31350779e-08 4.09159678e-10 9.17970056e-07 9.99985754e-01
  7.58156347e-08 3.30130389e-12 1.30684357e-05]
 [1.05364461e-05 2.44631490e-04 1.24785968e-03 7.01986064e-05
  4.98117704e-04 2.67122919e-03 9.95257378e-01]
 [8.43065280e-08 9.99901116e-01 7.18841946e-08 8.00116773e-09
  9.85569422e-05 2.76724532e-09 4.89725871e-10]
 [5.05022868e-08 1.49435914e-06 1.75062043e-04 2.31870578e-10
  1.08434935e-03 9.94900823e-01 3.83820152e-03]
 [1.24264359e-08 9.99864578e-01 4.57271682e-07 7.40434771e-08
  1.34907124e-04 3.68907327e-09 7.16965376e-09]
 [8.44000567e-08 4.59381067e-09 4.05355394e-08 9.99998987e-01
  1.81674622e-07 1.54569109e-12 5.37553603e-07]
 [1.02797934e-07 9.99625504e-01 3.48321549e-07 2.37206503e-08
  3.74082621e-04 5.61128672e-08 1.33409195e-08]
 [1.67241451e-05 3.02546495e-03 5.16984845e-03 4.00433419e-05
  9.89380538e-01 4.95336484e-04 1.87196722e-03]
 [9.99960244e-01 2.84082646e-10 3.97081276e-05 1.26955876e-10
  4.27145253e-08 1.00732707e-10 6.33287936e-11]
 [9.99993861e-01 9.77443348e-10 2.65771291e-06 3.34854553e-06
  6.39905906e-08 1.01706892e-12 5.15739063e-10]
 [5.42386202e-04 1.91148297e-06 9.98673320e-01 1.48296185e-05
  7.53004686e-04 3.18723323e-07 1.42170047e-05]
 [9.99970853e-01 2.68254752e-10 2.90166172e-05 4.10320361e-10
  1.41772858e-07 4.58295818e-10 4.35800146e-10]
 [2.31350779e-08 4.09159678e-10 9.17970056e-07 9.99985754e-01
  7.58156347e-08 3.30130389e-12 1.30684357e-05]
 [2.88974071e-08 5.87636748e-08 1.55182806e-05 1.94582761e-12
  9.28745867e-05 9.99862909e-01 2.87108214e-05]
 [1.24263879e-08 9.99864578e-01 4.57270801e-07 7.40433350e-08
  1.34906863e-04 3.68905928e-09 7.16965376e-09]
 [9.96088545e-08 9.99725580e-01 2.40599056e-05 5.09458182e-08
  2.49210338e-04 6.44892282e-07 4.95408585e-07]
 [9.44275946e-09 3.79351803e-08 2.63843045e-08 9.99996364e-01
  2.17737181e-07 5.36950822e-12 3.27758266e-06]
 [6.47993829e-07 1.19421820e-05 2.63737165e-04 1.02192455e-04
  5.77602113e-05 1.30761109e-04 9.99432921e-01]
 [8.61895728e-08 3.63079977e-07 8.47509073e-04 5.34510769e-10
  7.98463472e-04 9.57483947e-01 4.08696942e-02]
 [9.99994934e-01 7.79033229e-08 4.77607091e-06 2.54867483e-09
  8.42025827e-08 1.64556299e-10 1.06305520e-10]
 [9.99892652e-01 1.84037041e-09 1.06903215e-04 4.97316965e-10
  3.03374037e-07 2.64925903e-09 6.06367956e-10]
 [9.99713063e-01 2.49434973e-10 2.86683964e-04 5.43723955e-10
  1.95580796e-07 3.10739146e-09 1.66292424e-09]
 [2.00396388e-09 7.78424464e-07 3.89944034e-05 9.53544969e-12
  2.48716766e-04 9.97464955e-01 2.24659056e-03]
 [2.31350796e-08 4.09159706e-10 9.17970112e-07 9.99985814e-01
  7.58156418e-08 3.30130411e-12 1.30684366e-05]
 [4.75610200e-08 8.45136242e-07 9.03254841e-06 3.19190204e-12
  3.49372625e-04 9.99600947e-01 3.98259290e-05]
 [2.03767675e-03 7.54827352e-06 9.96469736e-01 6.06637400e-07
  1.39998784e-03 7.33480629e-05 1.11362160e-05]]
[[4.68069672e-08 9.99866247e-01 1.50920289e-06 9.34248590e-09
  1.31981200e-04 2.53384286e-07 3.34231807e-08]
 [2.36613484e-08 9.99964297e-01 2.40244418e-07 2.35388553e-09
  3.54334370e-05 8.92790819e-09 1.06643905e-09]
 [9.99983847e-01 2.37557707e-09 1.60234995e-05 1.45514101e-10
  9.02075996e-08 1.25780733e-10 2.95655167e-11]
 [9.99960244e-01 2.84082646e-10 3.97081276e-05 1.26955876e-10
  4.27145253e-08 1.00732707e-10 6.33287936e-11]
 [7.14975812e-09 8.86746232e-11 7.69566313e-08 9.99998271e-01
  8.96457486e-09 1.58959204e-13 1.57999750e-06]
 [1.55074318e-08 3.80016886e-11 1.63797381e-06 9.99994695e-01
  1.24140271e-08 3.48439458e-13 3.60273634e-06]
 [9.99997556e-01 2.89486723e-09 2.22689641e-06 1.44268611e-10
  7.86890482e-08 1.94717124e-11 7.16255917e-12]
 [5.49069306e-08 9.98675585e-01 7.06272403e-05 2.73462933e-06
  1.24586874e-03 1.89487182e-07 4.94792675e-06]
 [4.79785953e-08 4.34143260e-07 2.14042212e-03 1.50606311e-07
  1.23158388e-03 1.15208188e-02 9.85106528e-01]
 [9.99467147e-07 9.97832000e-01 1.06061398e-05 3.08742955e-07
  2.15175049e-03 3.44625300e-06 9.57089469e-07]
 [2.80600898e-06 9.99102354e-01 3.87644395e-05 3.34948197e-07
  8.55164835e-04 4.38081372e-07 1.22628421e-07]
 [3.87332315e-04 1.54138036e-07 9.98058140e-01 4.94299631e-04
  1.35860400e-05 3.14124463e-07 1.04606303e-03]
 [9.44275946e-09 3.79351803e-08 2.63843045e-08 9.99996364e-01
  2.17737181e-07 5.36950822e-12 3.27758266e-06]
 [2.23161396e-06 9.87678170e-01 8.99316001e-05 1.08850350e-06
  1.20543074e-02 1.39722644e-04 3.44977670e-05]
 [6.82858808e-05 1.20739728e-07 9.99574482e-01 3.66157042e-06
  3.05989641e-04 8.12568317e-07 4.66394522e-05]
 [5.42386202e-04 1.91148297e-06 9.98673320e-01 1.48296185e-05
  7.53004686e-04 3.18723323e-07 1.42170047e-05]
 [2.36613484e-08 9.99964297e-01 2.40244418e-07 2.35388553e-09
  3.54334370e-05 8.92790819e-09 1.06643905e-09]
 [2.31350779e-08 4.09159678e-10 9.17970056e-07 9.99985754e-01
  7.58156347e-08 3.30130389e-12 1.30684357e-05]
 [1.05364461e-05 2.44631490e-04 1.24785968e-03 7.01986064e-05
  4.98117704e-04 2.67122919e-03 9.95257378e-01]
 [8.43065280e-08 9.99901116e-01 7.18841946e-08 8.00116773e-09
  9.85569422e-05 2.76724532e-09 4.89725871e-10]
 [5.05022868e-08 1.49435914e-06 1.75062043e-04 2.31870578e-10
  1.08434935e-03 9.94900823e-01 3.83820152e-03]
 [1.24264359e-08 9.99864578e-01 4.57271682e-07 7.40434771e-08
  1.34907124e-04 3.68907327e-09 7.16965376e-09]
 [8.44000567e-08 4.59381067e-09 4.05355394e-08 9.99998987e-01
  1.81674622e-07 1.54569109e-12 5.37553603e-07]
 [1.02797934e-07 9.99625504e-01 3.48321549e-07 2.37206503e-08
  3.74082621e-04 5.61128672e-08 1.33409195e-08]
 [1.67241451e-05 3.02546495e-03 5.16984845e-03 4.00433419e-05
  9.89380538e-01 4.95336484e-04 1.87196722e-03]
 [9.99960244e-01 2.84082646e-10 3.97081276e-05 1.26955876e-10
  4.27145253e-08 1.00732707e-10 6.33287936e-11]
 [9.99993861e-01 9.77443348e-10 2.65771291e-06 3.34854553e-06
  6.39905906e-08 1.01706892e-12 5.15739063e-10]
 [5.42386202e-04 1.91148297e-06 9.98673320e-01 1.48296185e-05
  7.53004686e-04 3.18723323e-07 1.42170047e-05]
 [9.99970853e-01 2.68254752e-10 2.90166172e-05 4.10320361e-10
  1.41772858e-07 4.58295818e-10 4.35800146e-10]
 [2.31350779e-08 4.09159678e-10 9.17970056e-07 9.99985754e-01
  7.58156347e-08 3.30130389e-12 1.30684357e-05]
 [2.88974071e-08 5.87636748e-08 1.55182806e-05 1.94582761e-12
  9.28745867e-05 9.99862909e-01 2.87108214e-05]
 [1.24263879e-08 9.99864578e-01 4.57270801e-07 7.40433350e-08
  1.34906863e-04 3.68905928e-09 7.16965376e-09]
 [9.96088545e-08 9.99725580e-01 2.40599056e-05 5.09458182e-08
  2.49210338e-04 6.44892282e-07 4.95408585e-07]
 [9.44275946e-09 3.79351803e-08 2.63843045e-08 9.99996364e-01
  2.17737181e-07 5.36950822e-12 3.27758266e-06]
 [6.47993829e-07 1.19421820e-05 2.63737165e-04 1.02192455e-04
  5.77602113e-05 1.30761109e-04 9.99432921e-01]
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 [9.99983847e-01 2.37557707e-09 1.60234995e-05 1.45514101e-10
  9.02075996e-08 1.25780733e-10 2.95655167e-11]
 [9.99960244e-01 2.84082646e-10 3.97081276e-05 1.26955876e-10
  4.27145253e-08 1.00732707e-10 6.33287936e-11]
 [7.14975812e-09 8.86746232e-11 7.69566313e-08 9.99998271e-01
  8.96457486e-09 1.58959204e-13 1.57999750e-06]
 [1.55074318e-08 3.80016886e-11 1.63797381e-06 9.99994695e-01
  1.24140271e-08 3.48439458e-13 3.60273634e-06]
 [9.99997556e-01 2.89486723e-09 2.22689641e-06 1.44268611e-10
  7.86890482e-08 1.94717124e-11 7.16255917e-12]
 [5.49069306e-08 9.98675585e-01 7.06272403e-05 2.73462933e-06
  1.24586874e-03 1.89487182e-07 4.94792675e-06]
 [4.79785953e-08 4.34143260e-07 2.14042212e-03 1.50606311e-07
  1.23158388e-03 1.15208188e-02 9.85106528e-01]
 [9.99467147e-07 9.97832000e-01 1.06061398e-05 3.08742955e-07
  2.15175049e-03 3.44625300e-06 9.57089469e-07]
 [2.80600898e-06 9.99102354e-01 3.87644395e-05 3.34948197e-07
  8.55164835e-04 4.38081372e-07 1.22628421e-07]
 [3.87332315e-04 1.54138036e-07 9.98058140e-01 4.94299631e-04
  1.35860400e-05 3.14124463e-07 1.04606303e-03]
 [9.44275946e-09 3.79351803e-08 2.63843045e-08 9.99996364e-01
  2.17737181e-07 5.36950822e-12 3.27758266e-06]
 [2.23161396e-06 9.87678170e-01 8.99316001e-05 1.08850350e-06
  1.20543074e-02 1.39722644e-04 3.44977670e-05]
 [6.82858808e-05 1.20739728e-07 9.99574482e-01 3.66157042e-06
  3.05989641e-04 8.12568317e-07 4.66394522e-05]
 [5.42386202e-04 1.91148297e-06 9.98673320e-01 1.48296185e-05
  7.53004686e-04 3.18723323e-07 1.42170047e-05]
 [2.36613484e-08 9.99964297e-01 2.40244418e-07 2.35388553e-09
  3.54334370e-05 8.92790819e-09 1.06643905e-09]
 [2.31350779e-08 4.09159678e-10 9.17970056e-07 9.99985754e-01
  7.58156347e-08 3.30130389e-12 1.30684357e-05]
 [1.05364461e-05 2.44631490e-04 1.24785968e-03 7.01986064e-05
  4.98117704e-04 2.67122919e-03 9.95257378e-01]
 [8.43065280e-08 9.99901116e-01 7.18841946e-08 8.00116773e-09
  9.85569422e-05 2.76724532e-09 4.89725871e-10]
 [5.05022868e-08 1.49435914e-06 1.75062043e-04 2.31870578e-10
  1.08434935e-03 9.94900823e-01 3.83820152e-03]
 [1.24264359e-08 9.99864578e-01 4.57271682e-07 7.40434771e-08
  1.34907124e-04 3.68907327e-09 7.16965376e-09]
 [8.44000567e-08 4.59381067e-09 4.05355394e-08 9.99998987e-01
  1.81674622e-07 1.54569109e-12 5.37553603e-07]
 [1.02797934e-07 9.99625504e-01 3.48321549e-07 2.37206503e-08
  3.74082621e-04 5.61128672e-08 1.33409195e-08]
 [1.67241451e-05 3.02546495e-03 5.16984845e-03 4.00433419e-05
  9.89380538e-01 4.95336484e-04 1.87196722e-03]
 [9.99960244e-01 2.84082646e-10 3.97081276e-05 1.26955876e-10
  4.27145253e-08 1.00732707e-10 6.33287936e-11]
 [9.99993861e-01 9.77443348e-10 2.65771291e-06 3.34854553e-06
  6.39905906e-08 1.01706892e-12 5.15739063e-10]
 [5.42386202e-04 1.91148297e-06 9.98673320e-01 1.48296185e-05
  7.53004686e-04 3.18723323e-07 1.42170047e-05]
 [9.99970853e-01 2.68254752e-10 2.90166172e-05 4.10320361e-10
  1.41772858e-07 4.58295818e-10 4.35800146e-10]
 [2.31350779e-08 4.09159678e-10 9.17970056e-07 9.99985754e-01
  7.58156347e-08 3.30130389e-12 1.30684357e-05]
 [2.88974071e-08 5.87636748e-08 1.55182806e-05 1.94582761e-12
  9.28745867e-05 9.99862909e-01 2.87108214e-05]
 [1.24263879e-08 9.99864578e-01 4.57270801e-07 7.40433350e-08
  1.34906863e-04 3.68905928e-09 7.16965376e-09]
 [9.96088545e-08 9.99725580e-01 2.40599056e-05 5.09458182e-08
  2.49210338e-04 6.44892282e-07 4.95408585e-07]
 [9.44275946e-09 3.79351803e-08 2.63843045e-08 9.99996364e-01
  2.17737181e-07 5.36950822e-12 3.27758266e-06]
 [6.47993829e-07 1.19421820e-05 2.63737165e-04 1.02192455e-04
  5.77602113e-05 1.30761109e-04 9.99432921e-01]
 [8.61895728e-08 3.63079977e-07 8.47509073e-04 5.34510769e-10
  7.98463472e-04 9.57483947e-01 4.08696942e-02]
 [9.99994934e-01 7.79033229e-08 4.77607091e-06 2.54867483e-09
  8.42025827e-08 1.64556299e-10 1.06305520e-10]
 [9.99892652e-01 1.84037041e-09 1.06903215e-04 4.97316965e-10
  3.03374037e-07 2.64925903e-09 6.06367956e-10]
 [9.99713063e-01 2.49434973e-10 2.86683964e-04 5.43723955e-10
  1.95580796e-07 3.10739146e-09 1.66292424e-09]
 [2.00396388e-09 7.78424464e-07 3.89944034e-05 9.53544969e-12
  2.48716766e-04 9.97464955e-01 2.24659056e-03]
 [2.31350796e-08 4.09159706e-10 9.17970112e-07 9.99985814e-01
  7.58156418e-08 3.30130411e-12 1.30684366e-05]
 [4.75610200e-08 8.45136242e-07 9.03254841e-06 3.19190204e-12
  3.49372625e-04 9.99600947e-01 3.98259290e-05]
 [2.03767675e-03 7.54827352e-06 9.96469736e-01 6.06637400e-07
  1.39998784e-03 7.33480629e-05 1.11362160e-05]]


y_preds_ten[0]

array([4.6806967e-08, 9.9986625e-01, 1.5092029e-06, 9.3424859e-09,
       1.3198120e-04, 2.5338429e-07, 3.3423181e-08], dtype=float32)


# prediction array vs true array
y_preds_ten.argmax(axis=1), test_data_y_ten

(array([1, 1, 0, 0, 3, 3, 0, 1, 6, 1, 1, 2, 3, 1, 2, 2, 1, 3, 6, 1, 5, 1,
        3, 1, 4, 0, 0, 2, 0, 3, 5, 1, 1, 3, 6, 5, 0, 0, 0, 5, 3, 5, 2],
       dtype=int64),
 array([1, 1, 0, 0, 3, 3, 0, 1, 6, 1, 1, 2, 3, 1, 2, 2, 1, 3, 6, 1, 5, 1,
        3, 1, 4, 0, 0, 2, 0, 3, 5, 1, 1, 3, 6, 5, 0, 0, 0, 5, 3, 5, 2],
       dtype=int64))


# Create sk-learn cofusion matrix
from sklearn.metrics import confusion_matrix
confusion_matrix(y_true=test_data_y_ten, 
                 y_pred=y_preds_ten.argmax(axis=1))

array([[ 9,  0,  0,  0,  0,  0,  0],
       [ 0, 12,  0,  0,  0,  0,  0],
       [ 0,  0,  5,  0,  0,  0,  0],
       [ 0,  0,  0,  8,  0,  0,  0],
       [ 0,  0,  0,  0,  1,  0,  0],
       [ 0,  0,  0,  0,  0,  5,  0],
       [ 0,  0,  0,  0,  0,  0,  3]], dtype=int64)


# Make function for pretty confusion matrix
# Note: The following confusion matrix code is a remix of Scikit-Learn's 
# plot_confusion_matrix function - https://scikit-learn.org/stable/modules/generated/sklearn.metrics.plot_confusion_matrix.html
# and Made with ML's introductory notebook - https://github.com/GokuMohandas/MadeWithML/blob/main/notebooks/08_Neural_Networks.ipynb
import itertools
from sklearn.metrics import confusion_matrix

# Our function needs a different name to sklearn's plot_confusion_matrix
def make_confusion_matrix(y_true, y_pred, classes=None, figsize=(10, 10), text_size=15): 
  """Makes a labelled confusion matrix comparing predictions and ground truth labels.

  If classes is passed, confusion matrix will be labelled, if not, integer class values
  will be used.

  Args:
    y_true: Array of truth labels (must be same shape as y_pred).
    y_pred: Array of predicted labels (must be same shape as y_true).
    classes: Array of class labels (e.g. string form). If `None`, integer labels are used.
    figsize: Size of output figure (default=(10, 10)).
    text_size: Size of output figure text (default=15).
  
  Returns:
    A labelled confusion matrix plot comparing y_true and y_pred.

  Example usage:
    make_confusion_matrix(y_true=test_labels, # ground truth test labels
                          y_pred=y_preds, # predicted labels
                          classes=class_names, # array of class label names
                          figsize=(15, 15),
                          text_size=10)
  """  
  # Create the confustion matrix
  cm = confusion_matrix(y_true, y_pred)
  cm_norm = cm.astype("float") / cm.sum(axis=1)[:, np.newaxis] # normalize it
  n_classes = cm.shape[0] # find the number of classes we're dealing with

  # Plot the figure and make it pretty
  fig, ax = plt.subplots(figsize=figsize)
  cax = ax.matshow(cm, cmap=plt.cm.Blues) # colors will represent how 'correct' a class is, darker == better
  fig.colorbar(cax)

  # Are there a list of classes?
  if classes:
    labels = classes
  else:
    labels = np.arange(cm.shape[0])
  
  # Label the axes
  ax.set(title="Confusion Matrix",
         xlabel="Predicted label",
         ylabel="True label",
         xticks=np.arange(n_classes), # create enough axis slots for each class
         yticks=np.arange(n_classes), 
         xticklabels=labels, # axes will labeled with class names (if they exist) or ints
         yticklabels=labels)
  
  # Make x-axis labels appear on bottom
  ax.xaxis.set_label_position("bottom")
  ax.xaxis.tick_bottom()

  # Set the threshold for different colors
  threshold = (cm.max() + cm.min()) / 2.

  # Plot the text on each cell
  for i, j in itertools.product(range(cm.shape[0]), range(cm.shape[1])):
    plt.text(j, i, f"{cm[i, j]} ({cm_norm[i, j]*100:.1f}%)",
             horizontalalignment="center",
             color="white" if cm[i, j] > threshold else "black",
             size=text_size)


class_names = ["Mammal","Bird","Reptile","Fish","Amphibian","Bug","Invertebrate"]
make_confusion_matrix(y_true=test_data_y_ten, 
                      y_pred=y_preds_ten.argmax(axis=1),
                      classes=class_names,
                      figsize=(15, 15),
                      text_size=10)


# We can learn more about model with
model_7.summary()

Model: "sequential_9"
_________________________________________________________________
 Layer (type)                Output Shape              Param #   
=================================================================
 dense_27 (Dense)            (None, 100)               1700      
                                                                 
 dense_28 (Dense)            (None, 100)               10100     
                                                                 
 dense_29 (Dense)            (None, 7)                 707       
                                                                 
=================================================================
Total params: 12,507
Trainable params: 12,507
Non-trainable params: 0
_________________________________________________________________


# Find the layers of our most recent model
model_7.layers

[<keras.layers.core.dense.Dense at 0x2259c07f880>,
 <keras.layers.core.dense.Dense at 0x2259c6547c0>,
 <keras.layers.core.dense.Dense at 0x2259c654670>]


# Extract a particular layer
model_7.layers[1]

<keras.layers.core.dense.Dense at 0x2259c6547c0>


# Get the patterns of a layer in our network
weights, biases = model_7.layers[1].get_weights()
weights, weights.shape

(array([[-0.09261939, -0.09116485, -0.05604276, ...,  0.22362868,
          0.00486792, -0.1505617 ],
        [ 0.1279111 , -0.03129999,  0.15448469, ..., -0.07239661,
          0.09139528,  0.07953712],
        [ 0.05676463, -0.03698636, -0.05017348, ..., -0.20861612,
          0.04943918, -0.06877349],
        ...,
        [ 0.01721151, -0.0661222 ,  0.19543055, ...,  0.04433658,
         -0.01983955, -0.10782329],
        [ 0.21621093, -0.18269145,  0.08003305, ...,  0.02894781,
          0.09124424,  0.10404825],
        [ 0.09659153, -0.0761317 ,  0.1144512 , ...,  0.14968348,
          0.14994195, -0.06229505]], dtype=float32),
 (100, 100))

	animal_name	hair	feathers	eggs	milk	airborne	aquatic	predator	toothed	backbone	breathes	venomous	fins	legs	tail	domestic	catsize	Class_Index
0	aardvark	1	0	0	1	0	0	1	1	1	1	0	0	4	0	0	1	0
1	antelope	1	0	0	1	0	0	0	1	1	1	0	0	4	1	0	1	0
2	bass	0	0	1	0	0	1	1	1	1	0	0	1	0	1	0	0	3
3	bear	1	0	0	1	0	0	1	1	1	1	0	0	4	0	0	1	0
4	boar	1	0	0	1	0	0	1	1	1	1	0	0	4	1	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
65	vespa	0	0	1	0	1	0	1	0	0	1	1	0	6	0	0	0	5
66	bicho-pau	0	0	1	0	0	0	0	0	0	1	0	0	6	0	0	0	6
67	caracol-da-mata-atlantica	0	0	1	0	0	0	0	0	0	1	0	0	0	0	0	0	6
68	caranguejeira	1	0	1	0	0	0	1	0	0	1	1	0	8	0	0	0	6
69	sauva-limao	1	0	1	0	0	0	1	0	0	1	1	0	6	0	0	0	6

	Class_Number	Number_Of_Animal_Species_In_Class	Class_Type	Animal_Names	Class_Index
0	1	41	Mammal	aardvark, antelope, bear, boar, buffalo, calf,...	0
1	2	20	Bird	chicken, crow, dove, duck, flamingo, gull, haw...	1
2	3	5	Reptile	pitviper, seasnake, slowworm, tortoise, tuatara	2
3	4	13	Fish	bass, carp, catfish, chub, dogfish, haddock, h...	3
4	5	4	Amphibian	frog, frog, newt, toad	4
5	6	8	Bug	flea, gnat, honeybee, housefly, ladybird, moth...	5
6	7	10	Invertebrate	clam, crab, crayfish, lobster, octopus, scorpi...	6

	hair	feathers	eggs	milk	airborne	aquatic	predator	toothed	backbone	breathes	venomous	fins	legs	tail	domestic	catsize	Class_Index
0	1	0	0	1	0	0	1	1	1	1	0	0	4	0	0	1	0
1	1	0	0	1	0	0	0	1	1	1	0	0	4	1	0	1	0
2	1	0	0	1	0	0	1	1	1	1	0	0	4	0	0	1	0
3	1	0	0	1	0	0	1	1	1	1	0	0	4	1	0	1	0
4	1	0	0	1	0	0	0	1	1	1	0	0	4	1	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
209	0	0	1	0	0	0	1	1	1	1	1	0	0	1	0	0	2
210	0	0	1	0	0	0	1	1	1	1	0	0	0	1	0	0	2
211	0	0	1	0	0	1	1	1	1	1	0	0	0	1	0	0	2
212	0	0	1	0	0	1	0	0	1	1	0	0	4	0	1	0	2
213	0	0	1	0	0	0	1	1	1	1	1	0	0	1	0	0	2

	hair	feathers	eggs	milk	airborne	aquatic	predator	toothed	backbone	breathes	venomous	fins	legs	tail	domestic	catsize	Class_Index
0	1	0	0	1	0	0	1	1	1	1	0	0	4	0	0	1	0
1	1	0	0	1	0	0	0	1	1	1	0	0	4	1	0	1	0
2	1	0	0	1	0	0	1	1	1	1	0	0	4	0	0	1	0
3	1	0	0	1	0	0	1	1	1	1	0	0	4	1	0	1	0
4	1	0	0	1	0	0	0	1	1	1	0	0	4	1	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
209	0	0	1	0	0	0	1	1	1	1	1	0	0	1	0	0	2
210	0	0	1	0	0	0	1	1	1	1	0	0	0	1	0	0	2
211	0	0	1	0	0	1	1	1	1	1	0	0	0	1	0	0	2
212	0	0	1	0	0	1	0	0	1	1	0	0	4	0	1	0	2
213	0	0	1	0	0	0	1	1	1	1	1	0	0	1	0	0	2

	hair	feathers	eggs	milk	airborne	aquatic	predator	toothed	backbone	breathes	venomous	fins	legs	tail	domestic	catsize	Class_Index
0	1	0	0	1	0	0	1	1	1	1	0	0	4	0	0	1	0
1	1	0	0	1	0	0	0	1	1	1	0	0	4	1	0	1	0
2	1	0	0	1	0	0	1	1	1	1	0	0	4	0	0	1	0
3	1	0	0	1	0	0	1	1	1	1	0	0	4	1	0	1	0
4	1	0	0	1	0	0	0	1	1	1	0	0	4	1	0	1	0
...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...	...
209	0	0	1	0	0	0	1	1	1	1	1	0	0	1	0	0	2
210	0	0	1	0	0	0	1	1	1	1	0	0	0	1	0	0	2
211	0	0	1	0	0	1	1	1	1	1	0	0	0	1	0	0	2
212	0	0	1	0	0	1	0	0	1	1	0	0	4	0	1	0	2
213	0	0	1	0	0	0	1	1	1	1	1	0	0	1	0	0	2