File size: 2,376 Bytes
0f1dee6 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | # -*- coding: utf-8 -*-
"""Untitled8.ipynb
Automatically generated by Colaboratory.
Original file is located at
https://colab.research.google.com/drive/1VfD6Tk-uTrJOBlTJImKWkPqp3EPsrU6F
"""
import tensorflow as tf
import matplotlib.pyplot as plt
import seaborn as sn
import numpy as np
import pandas as pd
import math
import datetime
import platform
mnist_dataset = tf.keras.datasets.fashion_mnist
(x_train, y_train), (x_test, y_test) = mnist_dataset.load_data()
x_train_odd=x_train
y_train_odd=y_train
print('x_train:', x_train_odd.shape)
print('y_train:', y_train_odd.shape)
print('x_test:', x_test.shape)
print('y_test:', y_test.shape)
plt.imshow(x_test[0], cmap=plt.cm.binary)
plt.show()
x_train_normalized = x_train / 255
x_test_normalized = x_test / 255
model = tf.keras.models.Sequential()
# Input layers.
model.add(tf.keras.layers.Flatten(input_shape=x_train_normalized.shape[1:]))
model.add(tf.keras.layers.Dense(
units=256,
activation=tf.keras.activations.relu,
kernel_regularizer=tf.keras.regularizers.l2(0.002)
))
# Hidden layers.
model.add(tf.keras.layers.Dense(
units=256,
activation=tf.keras.activations.relu,
kernel_regularizer=tf.keras.regularizers.l2(0.002)
))
# Output layers.
model.add(tf.keras.layers.Dense(
units=10,
activation=tf.keras.activations.softmax
))
model.summary()
tf.keras.utils.plot_model(
model,
show_shapes=True,
show_layer_names=True,
)
adam_optimizer = tf.keras.optimizers.Adam(learning_rate=0.001)
model.compile(
optimizer=adam_optimizer,
loss=tf.keras.losses.sparse_categorical_crossentropy,
metrics=['accuracy']
)
log_dir=".logs/fit/" + datetime.datetime.now().strftime("%Y%m%d-%H%M%S")
tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir=log_dir, histogram_freq=1)
training_history = model.fit(
x_train_normalized,
y_train,
epochs=20,
validation_data=(x_test_normalized, y_test),
callbacks=[tensorboard_callback]
)
# Commented out IPython magic to ensure Python compatibility.
# %%capture
# train_loss, train_accuracy = model.evaluate(x_train_normalized, y_train)
# validation_loss, validation_accuracy = model.evaluate(x_test_normalized, y_test)
print('Training loss: ', train_loss)
print('Training accuracy: ', train_accuracy)
print('Validation loss: ', validation_loss)
print('Validation accuracy: ', validation_accuracy) |