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| import matplotlib as mpl
import matplotlib.pyplot as plt
import tensorflow as tf
from tensorflow import keras
from sklearn.preprocessing import StandardScaler
import numpy as np
fashion_mnist = keras.datasets.fashion_mnist
(x_train_all, y_train_all), (x_test, y_test) = fashion_mnist.load_data()
x_valid, x_train = x_train_all[:5000], x_train_all[5000:]
y_valid, y_train = y_train_all[:5000], y_train_all[5000:]
print(x_valid.shape, y_valid.shape)
print(x_train.shape, y_train.shape)
print(x_test.shape, y_test.shape)
scaler = StandardScaler()
x_train_scaled = scaler.fit_transform(
x_train.astype(np.float32).reshape(-1, 1)).reshape(-1, 28, 28)
x_valid_scaled = scaler.transform(
x_valid.astype(np.float32).reshape(-1, 1)).reshape(-1, 28, 28)
x_test_scaled = scaler.transform(
x_test.astype(np.float32).reshape(-1, 1)).reshape(-1, 28, 28)
print(np.max(x_train_scaled), np.min(x_train_scaled))
def show_single_image(img_arr):
plt.imshow(img_arr, cmap="binary")
plt.show()
show_single_image(x_train[0])
def show_imgs(n_rows, n_cols, x_data, y_data, class_names):
plt.figure(figsize = (n_cols * 1.4, n_rows * 1.6))
for row in range(n_rows):
for col in range(n_cols):
index = n_cols * row + col
plt.subplot(n_rows, n_cols, index + 1)
plt.imshow(x_data[index], cmap = "binary", interpolation = "nearest")
plt.axis('off')
plt.title(class_names[y_data[index]])
plt.show()
class_names = ['T-shirt', 'Trouser', 'Pullover', 'Dress', 'Coat', 'Sandal', 'Shirt', 'Sneaker', 'Bag', 'Ankle boot']
show_imgs(3, 5, x_train, y_train, class_names)
model = keras.models.Sequential([
keras.layers.Flatten(input_shape = [28, 28]),
keras.layers.Dense(300, activation = 'relu'),
keras.layers.Dense(100, activation = 'relu'),
keras.layers.Dense(10, activation = 'softmax')
])
model.compile(loss = 'sparse_categorical_crossentropy',
optimizer = "sgd",
metrics=["accuracy"])
model.summary()
history = model.fit(x_train, y_train, epochs = 10, validation_data = (x_valid, y_valid))
history.history
import pandas as pd
def plot_learning_curves(history):
pd.DataFrame(history.history).plot(figsize=(8,5))
plt.grid(True)
plt.gca().set_ylim(0, 5)
plt.show()
plot_learning_curves(history)
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