Split into two files, removed unused code

This commit is contained in:
Michael Mandl 2018-12-30 12:37:35 +01:00
parent 88a3924637
commit 54db131db2
2 changed files with 56 additions and 113 deletions

118
flow.py
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@ -1,128 +1,52 @@
#!/usr/bin/python3
"""My tensorflow keras playground"""
import tensorflow as tf
from tensorflow import keras
import numpy as np
import matplotlib.pyplot as plt
import random
from graph import plot_training_acc
print("Running TensorFlow", tf.__version__)
fashion_mnist = keras.datasets.fashion_mnist
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
train_images = train_images / 255.0
test_images = test_images / 255.0
class_names = [
"T-shirt/top",
"Trouser",
"Pullover",
"Dress",
"Coat",
"Sandal",
"Shirt",
"Sneaker",
"Bag",
"Ankle boot",
]
model = keras.Sequential(
def model():
model = keras.Sequential(
[
keras.layers.Flatten(input_shape=(28, 28)),
keras.layers.Dense(256, activation=tf.nn.relu),
keras.layers.Dense(128, activation=tf.nn.relu),
keras.layers.Dense(10, activation=tf.nn.softmax),
]
)
)
model.compile(
model.compile(
optimizer=tf.train.AdamOptimizer(),
loss="sparse_categorical_crossentropy",
metrics=["accuracy"],
)
)
return model
def plot_training(history):
acc = history.history["acc"]
val_acc = history.history["val_acc"]
if __name__ == "__main__":
fashion_mnist = keras.datasets.fashion_mnist
epochs = range(1, len(acc) + 1)
(train_images, train_labels), (test_images, test_labels) = fashion_mnist.load_data()
plt.plot(epochs, acc, "bo", label="Training acc")
plt.plot(epochs, val_acc, "b", label="Validation acc")
plt.title("Training and validation accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.legend()
train_images = train_images / 255.0
test_images = test_images / 255.0
plt.show()
model = model()
early_stop = keras.callbacks.EarlyStopping(monitor="val_loss", patience=5)
early_stop = keras.callbacks.EarlyStopping(monitor="val_loss", patience=5)
history = model.fit(
history = model.fit(
train_images,
train_labels,
epochs=64,
batch_size=512,
batch_size=1024,
validation_data=(test_images, test_labels),
callbacks=[early_stop],
)
plot_training(history)
predictions = model.predict(test_images)
def plot_image(i, predictions_array, true_label, img):
predictions_array, true_label, img = predictions_array[i], true_label[i], img[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
plt.imshow(img, cmap=plt.cm.binary)
predicted_label = np.argmax(predictions_array)
if predicted_label == true_label:
color = "blue"
else:
color = "red"
plt.xlabel(
"{} {:2.0f}% ({})".format(
class_names[predicted_label],
100 * np.max(predictions_array),
class_names[true_label],
),
color=color,
)
def plot_value_array(i, predictions_array, true_label):
predictions_array, true_label = predictions_array[i], true_label[i]
plt.grid(False)
plt.xticks([])
plt.yticks([])
thisplot = plt.bar(range(10), predictions_array, color="#777777")
plt.ylim([0, 1])
predicted_label = np.argmax(predictions_array)
thisplot[predicted_label].set_color("red")
thisplot[true_label].set_color("blue")
num_rows = 5
num_cols = 5
num_images = num_rows * num_cols
plt.figure(figsize=(2 * 2 * num_cols, 2 * num_rows))
for i in range(num_images):
image_idx = random.randint(0, len(test_images) - 1)
plt.subplot(num_rows, 2 * num_cols, 2 * i + 1)
plot_image(image_idx, predictions, test_labels, test_images)
plt.subplot(num_rows, 2 * num_cols, 2 * i + 2)
plot_value_array(image_idx, predictions, test_labels)
plt.show()
plot_training_acc(history)

19
graph.py Normal file
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@ -0,0 +1,19 @@
"""Tensorflow graphs"""
import matplotlib.pyplot as plt
def plot_training_acc(history):
"""Plot training and validation accuracy"""
acc = history.history["acc"]
val_acc = history.history["val_acc"]
epochs = range(1, len(acc) + 1)
plt.plot(epochs, acc, "bo", label="Training acc")
plt.plot(epochs, val_acc, "b", label="Validation acc")
plt.title("Training and validation accuracy")
plt.xlabel("Epochs")
plt.ylabel("Accuracy")
plt.legend()
plt.show()