# -*- coding: utf-8 -*-
"""quantumjump.195

Automatically generated by Colab.

Original file is located at
    https://colab.research.google.com/drive/1ajVqXJvko89LMeo0a9e_uB75te2fLwMY
"""

# Commented out IPython magic to ensure Python compatibility.
import tensorflow as tf
from tensorflow import keras
import matplotlib.pyplot as plt
# %matplotlib inline
import numpy as np

(X_train, y_train), (X_test, y_test) = keras.datasets.mnist.load_data()

len(X_train)

len(X_test)

X_train[0].shape

X_train[0]

plt.matshow(X_train[2])

y_train[2]

y_train[:5]

X_train.shape

X_train = X_train / 255
X_test = X_test / 255

X_train[0]

X_train_flattened = X_train.reshape(len(X_train),28*28)
X_test_flattened = X_test.reshape(len(X_test),28*28)

X_train_flattened.shape

X_train_flattened[0]

model = keras.Sequential([
    keras.layers.Dense(10, input_shape=(784,),activation='sigmoid')
])

model.compile(
    optimizer='adam',
    loss='sparse_categorical_crossentropy',
    metrics=['accuracy']
)
model.fit(X_train_flattened, y_train, epochs=5)

model.evaluate(X_test_flattened, y_test)

plt.matshow(X_test[0])

y_predicted = model.predict(X_test_flattened)
y_predicted[0]

np.argmax(y_predicted[1])

y_predicted_labels = [np.argmax(i) for i in y_predicted]
y_predicted_labels[:5]

y_test[:5]

cm = tf.math.confusion_matrix(labels=y_test,predictions=y_predicted_labels)
cm

import seaborn as sn
plt.figure(figsize = (10,7))
sn.heatmap(cm, annot=True, fmt='d')
plt.xlabel('Predicted')
plt.ylabel('Truth')

model = keras.Sequential([
  keras.layers.Dense(100, input_shape=(784,), activation='relu'),
  keras.layers.Dense(10, activation='sigmoid')
])

model.compile(
  optimizer='adam',
  loss='sparse_categorical_crossentropy',
  metrics=['accuracy']
)
model.fit(X_train_flattened, y_train, epochs=5)

model.evaluate(X_test_flattened,y_test)

y_predicted = model.predict(X_test_flattened)
y_predicted_labels = [np.argmax(i) for i in y_predicted]
cm = tf.math.confusion_matrix(labels=y_test, predictions=y_predicted_labels)

plt.figure(figsize = (10,7))
sn.heatmap(cm, annot=True, fmt='d')
plt.xlabel('Predicted')
plt.ylabel('Truth')

model = keras.Sequential([
    keras.layers.Dense(100, input_shape=(784,),activation='relu'),
    keras.layers.Dense(10, activation='sigmoid')
])

model.compile(
  optimizer = 'adam',
  loss='sparse_categorical_crossentropy',
  metrics=['accuracy']
)

model.fit(X_train_flattened, y_train, epochs=5)