Create ML

Projects/ML

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+import random
+import matplotlib.pyplot as plt
+import numpy as np
+import pandas as pd
+import tensorflow as tf
+
+random.seed(0)
+np.random.seed(seed=0)
+tf.random.set_seed(seed=0)
+
+(x_train, y_train), (x_test, y_test ) = tf.keras.datasets.mnist.load_data()
+x_train.shape, y_train.shape, x_test.shape, y_test.shape
+
+print(
+    f'Training Size - Inputs:{x_train.shape}, Targets:{y_train.shape}'
+    f'\nTest Size - Inputs:{x_test.shape}, Targets:{y_test.shape}'
+)
+
+rows = 5
+digits_per_row = 5
+
+fig, axes = plt.subplots(nrows=rows, ncols=digits_per_row, figsize=(6, 6))
+axes = axes.flatten()
+
+# Selecting random ids from 0 to 60000
+total_digits = rows*digits_per_row
+random_ids= np.random.choice(x_train.shape[0], total_digits, replace=False)
+
+# Plotting the selected digits.
+for i, ax in enumerate(axes):
+    idx = random_ids[i]
+    ax.imshow(x_train[idx], cmap='gray')
+    ax.set_title(f'Class: {y_train[idx]}')
+    ax.axis('off')
+plt.tight_layout()
+plt.show()
+
+x_train = x_train.reshape((60000, 784))
+y_train = tf.one_hot(y_train, depth=10)
+x_test = x_test.reshape((10000, 784))
+y_test = tf.one_hot(y_test, depth=10)
+
+print(
+    f'Training Size - Inputs:{x_train.shape}, Targets:{y_train.shape}'
+    f'\nTest Size - Inputs:{x_test.shape}, Targets:{y_test.shape}'
+)
+
+activation = 'tanh'
+loss = 'categorical_crossentropy' # Do not change this loss function.
+metrics = ['accuracy']
+learning_rate = 0.001
+optimizer = tf.keras.optimizers.SGD(learning_rate=learning_rate) # Do not change this optimizer.
+epochs = 10
+
+model = tf.keras.Sequential([
+    tf.keras.layers.Input(shape=(784,), name='input'),
+    tf.keras.layers.Dense(units=256, activation=activation, name='hidden-1'),
+    tf.keras.layers.Dense(units=256, activation=activation, name='hidden-2'),
+    tf.keras.layers.Dense(units=10, activation='softmax', name='outputs') # Do not change this activation function.
+])
+model.summary(expand_nested=True)
+
+model.compile(optimizer=optimizer, loss=loss, metrics=metrics)
+history = model.fit(
+    x=x_train,
+    y=y_train,
+    epochs=epochs,
+    validation_data=(x_test, y_test)
+)
+
+train_loss = history.history['loss']
+val_loss = history.history['val_loss']
+train_acc = history.history['accuracy']
+val_acc = history.history['val_accuracy']
+
+plt.plot(train_loss, label='Train Loss')
+plt.plot(val_loss, label='Validation Loss')
+plt.title('Neural Network Loss per epoch')
+plt.ylabel('Categorical Cross-Entropy')
+plt.xlabel('Epochs')
+plt.xlim(0, epochs)
+plt.ylim(0, 1)
+plt.legend()
+plt.show()
+
+plt.plot(train_acc, label='Train Accuracy')
+plt.plot(val_acc, label='Validation Accuracy')
+plt.title('Neural Network Accuracy per epoch')
+plt.ylabel('Accuracy')
+plt.xlabel('Epochs')
+plt.xlim(0, epochs)
+plt.ylim(0, 1)
+plt.legend()
+plt.show()
+