[PYTHON] Die Geschichte des tiefen Lernens mit TPU
Hallo, das ist Sanjo. Ich habe in einer Gruppenarbeit an der Universität tiefgreifend gelernt, das ist also ein Rekord.
Überblick
Eigentlich haben wir mit dem von uns erstellten Datensatz tiefgreifend gelernt, aber in diesem Artikel werden wir der Einfachheit halber cifar10 verwenden. Lassen Sie uns mit CNN trainieren und das erstellte Modell visuell bewerten.
Entwicklungsumgebung
Google Colaboratory + TPU + tensorflow 2.0.0 (Google-Konto erforderlich)
Ändern Sie die Laufzeit in Python3 + TPU und installieren Sie Tensorflow 2.0.0 mit ! Pip install tensorflow == 2.0.0.
Ich werde es vorerst versuchen
gesamter Code
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers \
import Conv2D, MaxPool2D, GlobalAvgPool2D, Dense, \
BatchNormalization, ReLU, Softmax
from tensorflow.keras.datasets.cifar10 import load_data
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.utils import to_categorical
import pickle
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
os.environ['TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD'] = '10737418240'
def create_model():
model = Sequential([
Conv2D(filters=64, kernel_size=3, strides=1, padding='same', input_shape=(32,32,3)),
BatchNormalization(), ReLU(),
Conv2D(filters=64, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
Conv2D(filters=128, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=128, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
GlobalAvgPool2D(),
Dense(units=4096),
BatchNormalization(), ReLU(),
Dense(units=4096),
BatchNormalization(), ReLU(),
Dense(units=10),
BatchNormalization(), ReLU(),
Softmax()
])
return model
def data_augmentation(img):
im = tf.image.random_flip_left_right(img)
im = tf.image.random_flip_up_down(im)
im = tf.pad(im, tf.constant([[2, 2], [2, 2], [0, 0]]), "REFLECT")
im = tf.image.random_crop(im, size=[32, 32, 3])
return im
def train(batch_size, epochs, lr):
# Setup tpu
print("Setting up TPU ...")
tpu_grpc_url = "grpc://" + os.environ["COLAB_TPU_ADDR"]
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu_grpc_url)
tf.config.experimental_connect_to_cluster(tpu_cluster_resolver)
tf.tpu.experimental.initialize_tpu_system(tpu_cluster_resolver)
strategy = tf.distribute.experimental.TPUStrategy(tpu_cluster_resolver)
print("Done!")
# Load dataset
(x_train, y_train), (x_test, y_test) = load_data()
x_val, y_val = x_test[:5000], y_test[:5000]
x_test, y_test = x_test[5000:10000], y_test[5000:10000]
train_num = len(x_train)
val_num = len(x_val)
test_num = len(x_test)
# Normalize, convert to one-hot
x_train, y_train = x_train.astype(np.float32), y_train.astype(np.int32)
x_train /= 255.0
x_val, y_val = x_val.astype(np.float32), y_val.astype(np.int32)
x_val /= 255.0
x_test, y_test = x_test.astype(np.float32), y_test.astype(np.int32)
x_test /= 255.0
y_train = to_categorical(y_train, num_classes=10, dtype=np.int32)
y_val = to_categorical(y_val, num_classes=10, dtype=np.int32)
y_test = to_categorical(y_test, num_classes=10, dtype=np.int32)
# Convert to tf.data
trainset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
trainset = trainset.map(lambda image, label: (data_augmentation(image), label)).shuffle(buffer_size=1024).repeat().batch(batch_size)
valset = tf.data.Dataset.from_tensor_slices((x_val, y_val))
valset = valset.shuffle(buffer_size=1024).batch(val_num)
testset = tf.data.Dataset.from_tensor_slices((x_test, y_test))
testset = testset.batch(test_num)
# Train
callbacks = []
callbacks.append(tf.keras.callbacks.EarlyStopping('val_loss', patience=10))
op = SGD(lr=lr, momentum=0.9)
with strategy.scope():
model = create_model()
model.compile(optimizer=op, loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(
trainset, epochs=epochs,
validation_data=valset, callbacks=callbacks,
steps_per_epoch=train_num // batch_size
)
model.evaluate(testset)
model.save('model.h5')
# Save history
save_file = 'history.pkl'
with open(save_file, 'wb') as f:
pickle.dump(history.history, f)
print(f"Saving history to \'{save_file}\'. (epochs: {len(history.epoch)})")
train(128, 100, 0.01)
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers \
import Conv2D, MaxPool2D, GlobalAvgPool2D, Dense, \
BatchNormalization, ReLU, Softmax
from tensorflow.keras.datasets.cifar10 import load_data
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.utils import to_categorical
import pickle
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
os.environ['TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD'] = '10737418240'
def create_model():
model = Sequential([
Conv2D(filters=64, kernel_size=3, strides=1, padding='same', input_shape=(32,32,3)),
BatchNormalization(), ReLU(),
Conv2D(filters=64, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
Conv2D(filters=128, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=128, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
GlobalAvgPool2D(),
Dense(units=4096),
BatchNormalization(), ReLU(),
Dense(units=4096),
BatchNormalization(), ReLU(),
Dense(units=10),
BatchNormalization(), ReLU(),
Softmax()
])
return model
def data_augmentation(img):
im = tf.image.random_flip_left_right(img)
im = tf.image.random_flip_up_down(im)
im = tf.pad(im, tf.constant([[2, 2], [2, 2], [0, 0]]), "REFLECT")
im = tf.image.random_crop(im, size=[32, 32, 3])
return im
def train(batch_size, epochs, lr):
# Setup tpu
print("Setting up TPU ...")
tpu_grpc_url = "grpc://" + os.environ["COLAB_TPU_ADDR"]
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu_grpc_url)
tf.config.experimental_connect_to_cluster(tpu_cluster_resolver)
tf.tpu.experimental.initialize_tpu_system(tpu_cluster_resolver)
strategy = tf.distribute.experimental.TPUStrategy(tpu_cluster_resolver)
print("Done!")
# Load dataset
(x_train, y_train), (x_test, y_test) = load_data()
x_val, y_val = x_test[:5000], y_test[:5000]
x_test, y_test = x_test[5000:10000], y_test[5000:10000]
train_num = len(x_train)
val_num = len(x_val)
test_num = len(x_test)
# Normalize, convert to one-hot
x_train, y_train = x_train.astype(np.float32), y_train.astype(np.int32)
x_train /= 255.0
x_val, y_val = x_val.astype(np.float32), y_val.astype(np.int32)
x_val /= 255.0
x_test, y_test = x_test.astype(np.float32), y_test.astype(np.int32)
x_test /= 255.0
y_train = to_categorical(y_train, num_classes=10, dtype=np.int32)
y_val = to_categorical(y_val, num_classes=10, dtype=np.int32)
y_test = to_categorical(y_test, num_classes=10, dtype=np.int32)
# Convert to tf.data
trainset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
trainset = trainset.map(lambda image, label: (data_augmentation(image), label)).shuffle(buffer_size=1024).repeat().batch(batch_size)
valset = tf.data.Dataset.from_tensor_slices((x_val, y_val))
valset = valset.shuffle(buffer_size=1024).batch(val_num)
testset = tf.data.Dataset.from_tensor_slices((x_test, y_test))
testset = testset.batch(test_num)
# Train
callbacks = []
callbacks.append(tf.keras.callbacks.EarlyStopping('val_loss', patience=10))
op = SGD(lr=lr, momentum=0.9)
with strategy.scope():
model = create_model()
model.compile(optimizer=op, loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(
trainset, epochs=epochs,
validation_data=valset, callbacks=callbacks,
steps_per_epoch=train_num // batch_size
)
model.evaluate(testset)
model.save('model.h5')
# Save history
save_file = 'history.pkl'
with open(save_file, 'wb') as f:
pickle.dump(history.history, f)
print(f"Saving history to \'{save_file}\'. (epochs: {len(history.epoch)})")
train(128, 100, 0.01)
Machen Sie eine CNN
Bevor Sie es machen, schreiben Sie zuerst verschiedene Importe.
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers \
import Conv2D, MaxPool2D, GlobalAvgPool2D, Dense, \
BatchNormalization, ReLU, Softmax
from tensorflow.keras.datasets.cifar10 import load_data
from tensorflow.keras.optimizers import SGD
from tensorflow.keras.utils import to_categorical
import pickle
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
os.environ['TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD'] = '10737418240'
Wenn Sie der Meinung sind, dass das TPU-Einstellungsprotokoll verrauscht ist, lösen Sie es mit tf.compat.v1.logging.set_verbosity (tf.compat.v1.logging.WARN)!
Die letzten beiden Zeilen sind ebenfalls laut, daher sind sie stumm.
Lassen Sie uns nun ein geeignetes CNN basierend auf VGG16 erstellen.
model = Sequential([
Conv2D(filters=64, kernel_size=3, strides=1, padding='same', input_shape=(32,32,3)),
BatchNormalization(), ReLU(),
Conv2D(filters=64, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
Conv2D(filters=128, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=128, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
GlobalAvgPool2D(),
Dense(units=4096),
BatchNormalization(), ReLU(),
Dense(units=4096),
BatchNormalization(), ReLU(),
Dense(units=10),
BatchNormalization(), ReLU(),
Softmax()
])
So etwas (kleines Durchschnittsgefühl). Der Unterschied zur ursprünglichen Familie besteht hauptsächlich in der Chargennormalisierung. Wenn Sie dies beißen, wird sich die Genauigkeit erheblich unterscheiden. GlobalAvgPool ist nur enthalten, um die Form mit Dense abzugleichen, sodass es sich um Flatten oder Reshape handeln kann.
TPU verfügbar machen
Stellen Sie die TPU ein.
# Setup tpu
print("Setting up TPU ...")
tpu_grpc_url = "grpc://" + os.environ["COLAB_TPU_ADDR"]
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu_grpc_url)
tf.config.experimental_connect_to_cluster(tpu_cluster_resolver)
tf.tpu.experimental.initialize_tpu_system(tpu_cluster_resolver)
strategy = tf.distribute.experimental.TPUStrategy(tpu_cluster_resolver)
print("Done!")
Da es ein Synonym oder ähnliches ist, werde ich die Details weglassen. Da sich 2.0.0 noch im experimentellen Stadium befindet, besteht die Möglichkeit, dass "experimentell" in Zukunft entfernt wird.
Prozess cifar10
Holen Sie sich den Datensatz. Da nur Zugdaten und Testdaten aufbereitet werden, werden Testdaten aufgeteilt und Validierungsdaten aufbereitet.
# Load dataset
(x_train, y_train), (x_test, y_test) = load_data()
x_val, y_val = x_test[:5000], y_test[:5000]
x_test, y_test = x_test[5000:10000], y_test[5000:10000]
train_num = len(x_train)
val_num = len(x_val)
test_num = len(x_test)
cifar10 kann mit tf.keras.datasets.cifar10.load_data () erhalten werden. Wenn Sie es herunterladen, wird es für Sie erledigt, sodass Sie es nicht selbst ziehen oder Probleme machen müssen.
Bitte beachten Sie, dass die erfassten Daten nicht normalisiert sind und nicht in einer einzigen Darstellung dargestellt werden. Daher werden Normalisierung und One-Hot-Konvertierung angewendet.
# Normalize, convert to one-hot
x_train, y_train = x_train.astype(np.float32), y_train.astype(np.int32)
x_train /= 255.0
x_val, y_val = x_val.astype(np.float32), y_val.astype(np.int32)
x_val /= 255.0
x_test, y_test = x_test.astype(np.float32), y_test.astype(np.int32)
x_test /= 255.0
y_train = to_categorical(y_train, num_classes=10, dtype=np.int32)
y_val = to_categorical(y_val, num_classes=10, dtype=np.int32)
y_test = to_categorical(y_test, num_classes=10, dtype=np.int32)
Wenn Sie den Datentyp nicht richtig angeben, werden Sie bei der Verwendung von TPU wütend. Seien Sie also vorsichtig. Der Hauptgrund ist, dass TPU uint8 nicht unterstützt.
Darüber hinaus wird tf.data wie Tensorflow 2.0.0 verwendet. Das ist mein Lieblings.
# Convert to tf.data
trainset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
trainset = trainset.map(lambda image, label: (data_augmentation(image), label)).shuffle(buffer_size=1024).repeat().batch(batch_size)
valset = tf.data.Dataset.from_tensor_slices((x_val, y_val))
valset = valset.shuffle(buffer_size=1024).batch(val_num)
testset = tf.data.Dataset.from_tensor_slices((x_test, y_test))
testset = testset.batch(test_num)
Wenn Sie den Numpy-Tensor in das tf.data-Format konvertiert haben, mischen Sie ihn grob und legen Sie die Stapelgröße beiseite. Einzelheiten finden Sie in der offiziellen Dokumentation.
Lernen
# Train
callbacks = []
callbacks.append(tf.keras.callbacks.EarlyStopping('val_loss', patience=10))
op = SGD(lr=lr, momentum=0.9)
with strategy.scope():
model = create_model()
model.compile(optimizer=op, loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(
trainset, epochs=epochs,
validation_data=valset, callbacks=callbacks,
steps_per_epoch=train_num // batch_size
)
model.evaluate(testset)
model.save('model.h5')
# Save history
save_file = 'history.pkl'
with open(save_file, 'wb') as f:
pickle.dump(history.history, f)
print(f"Saving history to \'{save_file}\'. (epochs: {len(history.epoch)})")
Stellen Sie Early Stop für den Rückruf so ein, dass er stoppt, wenn das Lernen stagniert. Zusätzlich wird Moment SGD als Optimierer übernommen.
Wenn Sie TPU verwenden, fügen Sie es in with Strategy.scope (): ein. Da es sich diesmal um einen One-Hot-Ausdruck handelt, verwenden wir außerdem categoryical_crossentropy. Wenn Sie einfach die richtigen Antworten auflisten, verwenden Sie im Gegenteil sparse_categorical_crossentropy.
Lassen Sie als nächstes das Modell fit trainieren. Da tf.data verwendet wird, wird step_per_epoch wütend, wenn Sie es nicht richtig angeben. Bewerten Sie abschließend das Modell mit Testdaten und speichern Sie die Modellkonfiguration. Speichern Sie übrigens auch das Protokoll.
Ausführungsergebnis
Setting up TPU ...
INFO:tensorflow:Initializing the TPU system: 10.111.159.218:8470
INFO:tensorflow:Initializing the TPU system: 10.111.159.218:8470
INFO:tensorflow:Clearing out eager caches
INFO:tensorflow:Clearing out eager caches
INFO:tensorflow:Finished initializing TPU system.
INFO:tensorflow:Finished initializing TPU system.
INFO:tensorflow:Found TPU system:
INFO:tensorflow:Found TPU system:
INFO:tensorflow:*** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
Done!
Train on 390 steps, validate on 1 steps
Epoch 1/100
390/390 [==============================] - 28s 71ms/step - loss: 1.6188 - accuracy: 0.4342 - val_loss: 2.0326 - val_accuracy: 0.2442
Epoch 2/100
390/390 [==============================] - 11s 29ms/step - loss: 1.2757 - accuracy: 0.5581 - val_loss: 1.9118 - val_accuracy: 0.4016
Epoch 3/100
390/390 [==============================] - 11s 29ms/step - loss: 1.1264 - accuracy: 0.6105 - val_loss: 1.2568 - val_accuracy: 0.5498
Epoch 4/100
390/390 [==============================] - 11s 29ms/step - loss: 1.0150 - accuracy: 0.6484 - val_loss: 1.3001 - val_accuracy: 0.5564
Epoch 5/100
390/390 [==============================] - 11s 29ms/step - loss: 0.9265 - accuracy: 0.6820 - val_loss: 0.9689 - val_accuracy: 0.6802
Epoch 6/100
390/390 [==============================] - 11s 29ms/step - loss: 0.8634 - accuracy: 0.7041 - val_loss: 1.3286 - val_accuracy: 0.5624
Epoch 7/100
390/390 [==============================] - 11s 29ms/step - loss: 0.8024 - accuracy: 0.7261 - val_loss: 1.0214 - val_accuracy: 0.6676
Epoch 8/100
390/390 [==============================] - 11s 28ms/step - loss: 0.7602 - accuracy: 0.7416 - val_loss: 0.9298 - val_accuracy: 0.6804
Epoch 9/100
390/390 [==============================] - 11s 29ms/step - loss: 0.7220 - accuracy: 0.7575 - val_loss: 0.9745 - val_accuracy: 0.6784
Epoch 10/100
390/390 [==============================] - 11s 29ms/step - loss: 0.6771 - accuracy: 0.7717 - val_loss: 0.7908 - val_accuracy: 0.7244
Epoch 11/100
390/390 [==============================] - 11s 29ms/step - loss: 0.6520 - accuracy: 0.7819 - val_loss: 0.9043 - val_accuracy: 0.6934
Epoch 12/100
390/390 [==============================] - 11s 29ms/step - loss: 0.6146 - accuracy: 0.7947 - val_loss: 0.7787 - val_accuracy: 0.7446
Epoch 13/100
390/390 [==============================] - 11s 28ms/step - loss: 0.5933 - accuracy: 0.7997 - val_loss: 0.7505 - val_accuracy: 0.7528
Epoch 14/100
390/390 [==============================] - 11s 28ms/step - loss: 0.5710 - accuracy: 0.8072 - val_loss: 0.7057 - val_accuracy: 0.7644
Epoch 15/100
390/390 [==============================] - 11s 29ms/step - loss: 0.5457 - accuracy: 0.8174 - val_loss: 0.8235 - val_accuracy: 0.7312
Epoch 16/100
390/390 [==============================] - 11s 29ms/step - loss: 0.5243 - accuracy: 0.8262 - val_loss: 0.6631 - val_accuracy: 0.7672
Epoch 17/100
390/390 [==============================] - 11s 28ms/step - loss: 0.5073 - accuracy: 0.8307 - val_loss: 0.6935 - val_accuracy: 0.7734
Epoch 18/100
390/390 [==============================] - 11s 29ms/step - loss: 0.4879 - accuracy: 0.8380 - val_loss: 0.6700 - val_accuracy: 0.7748
Epoch 19/100
390/390 [==============================] - 11s 28ms/step - loss: 0.4698 - accuracy: 0.8414 - val_loss: 0.6904 - val_accuracy: 0.7714
Epoch 20/100
390/390 [==============================] - 11s 29ms/step - loss: 0.4508 - accuracy: 0.8479 - val_loss: 0.6207 - val_accuracy: 0.8012
Epoch 21/100
390/390 [==============================] - 12s 31ms/step - loss: 0.4416 - accuracy: 0.8536 - val_loss: 0.7352 - val_accuracy: 0.7684
Epoch 22/100
390/390 [==============================] - 11s 29ms/step - loss: 0.4235 - accuracy: 0.8586 - val_loss: 0.9226 - val_accuracy: 0.7102
Epoch 23/100
390/390 [==============================] - 11s 29ms/step - loss: 0.4118 - accuracy: 0.8635 - val_loss: 0.6872 - val_accuracy: 0.7864
Epoch 24/100
390/390 [==============================] - 11s 29ms/step - loss: 0.4054 - accuracy: 0.8633 - val_loss: 0.6349 - val_accuracy: 0.7980
Epoch 25/100
390/390 [==============================] - 11s 29ms/step - loss: 0.3873 - accuracy: 0.8716 - val_loss: 0.5795 - val_accuracy: 0.8112
Epoch 26/100
390/390 [==============================] - 11s 29ms/step - loss: 0.3743 - accuracy: 0.8762 - val_loss: 0.7991 - val_accuracy: 0.7580
Epoch 27/100
390/390 [==============================] - 11s 28ms/step - loss: 0.3623 - accuracy: 0.8811 - val_loss: 0.6132 - val_accuracy: 0.8048
Epoch 28/100
390/390 [==============================] - 11s 28ms/step - loss: 0.3537 - accuracy: 0.8821 - val_loss: 0.6159 - val_accuracy: 0.8034
Epoch 29/100
390/390 [==============================] - 11s 29ms/step - loss: 0.3451 - accuracy: 0.8871 - val_loss: 0.5890 - val_accuracy: 0.8172
Epoch 30/100
390/390 [==============================] - 11s 29ms/step - loss: 0.3341 - accuracy: 0.8888 - val_loss: 0.5170 - val_accuracy: 0.8312
Epoch 31/100
390/390 [==============================] - 11s 29ms/step - loss: 0.3217 - accuracy: 0.8930 - val_loss: 0.6331 - val_accuracy: 0.7992
Epoch 32/100
390/390 [==============================] - 11s 29ms/step - loss: 0.3161 - accuracy: 0.8941 - val_loss: 0.6867 - val_accuracy: 0.7958
Epoch 33/100
390/390 [==============================] - 11s 29ms/step - loss: 0.3062 - accuracy: 0.8975 - val_loss: 0.6233 - val_accuracy: 0.8064
Epoch 34/100
390/390 [==============================] - 11s 28ms/step - loss: 0.3011 - accuracy: 0.8990 - val_loss: 0.5072 - val_accuracy: 0.8326
Epoch 35/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2936 - accuracy: 0.9028 - val_loss: 0.5389 - val_accuracy: 0.8330
Epoch 36/100
390/390 [==============================] - 11s 28ms/step - loss: 0.2840 - accuracy: 0.9071 - val_loss: 0.6223 - val_accuracy: 0.8054
Epoch 37/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2734 - accuracy: 0.9085 - val_loss: 0.5799 - val_accuracy: 0.8176
Epoch 38/100
390/390 [==============================] - 11s 28ms/step - loss: 0.2720 - accuracy: 0.9092 - val_loss: 0.5513 - val_accuracy: 0.8324
Epoch 39/100
390/390 [==============================] - 11s 28ms/step - loss: 0.2625 - accuracy: 0.9120 - val_loss: 0.6154 - val_accuracy: 0.8116
Epoch 40/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2576 - accuracy: 0.9140 - val_loss: 0.5031 - val_accuracy: 0.8404
Epoch 41/100
390/390 [==============================] - 11s 28ms/step - loss: 0.2503 - accuracy: 0.9162 - val_loss: 0.5218 - val_accuracy: 0.8374
Epoch 42/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2441 - accuracy: 0.9186 - val_loss: 0.5802 - val_accuracy: 0.8216
Epoch 43/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2317 - accuracy: 0.9229 - val_loss: 0.5359 - val_accuracy: 0.8374
Epoch 44/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2345 - accuracy: 0.9208 - val_loss: 0.5335 - val_accuracy: 0.8386
Epoch 45/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2283 - accuracy: 0.9241 - val_loss: 0.4964 - val_accuracy: 0.8444
Epoch 46/100
390/390 [==============================] - 11s 28ms/step - loss: 0.2186 - accuracy: 0.9274 - val_loss: 0.6013 - val_accuracy: 0.8216
Epoch 47/100
390/390 [==============================] - 11s 28ms/step - loss: 0.2146 - accuracy: 0.9275 - val_loss: 0.5554 - val_accuracy: 0.8246
Epoch 48/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2064 - accuracy: 0.9315 - val_loss: 0.4062 - val_accuracy: 0.8742
Epoch 49/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2040 - accuracy: 0.9327 - val_loss: 0.5200 - val_accuracy: 0.8416
Epoch 50/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1994 - accuracy: 0.9340 - val_loss: 0.4557 - val_accuracy: 0.8644
Epoch 51/100
390/390 [==============================] - 11s 29ms/step - loss: 0.1911 - accuracy: 0.9371 - val_loss: 0.5573 - val_accuracy: 0.8282
Epoch 52/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1883 - accuracy: 0.9366 - val_loss: 0.5349 - val_accuracy: 0.8404
Epoch 53/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1818 - accuracy: 0.9392 - val_loss: 0.6524 - val_accuracy: 0.8090
Epoch 54/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1822 - accuracy: 0.9387 - val_loss: 0.5043 - val_accuracy: 0.8514
Epoch 55/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1825 - accuracy: 0.9401 - val_loss: 0.5196 - val_accuracy: 0.8500
Epoch 56/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1696 - accuracy: 0.9433 - val_loss: 0.4863 - val_accuracy: 0.8546
Epoch 57/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1662 - accuracy: 0.9452 - val_loss: 0.6238 - val_accuracy: 0.8246
Epoch 58/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1617 - accuracy: 0.9466 - val_loss: 0.5467 - val_accuracy: 0.8404
1/1 [==============================] - 2s 2s/step - loss: 0.5631 - accuracy: 0.8316
Saving history to 'history.pkl'. (epochs: 58)
Setting up TPU ...
INFO:tensorflow:Initializing the TPU system: 10.111.159.218:8470
INFO:tensorflow:Initializing the TPU system: 10.111.159.218:8470
INFO:tensorflow:Clearing out eager caches
INFO:tensorflow:Clearing out eager caches
INFO:tensorflow:Finished initializing TPU system.
INFO:tensorflow:Finished initializing TPU system.
INFO:tensorflow:Found TPU system:
INFO:tensorflow:Found TPU system:
INFO:tensorflow:*** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
Done!
Train on 390 steps, validate on 1 steps
Epoch 1/100
390/390 [==============================] - 28s 71ms/step - loss: 1.6188 - accuracy: 0.4342 - val_loss: 2.0326 - val_accuracy: 0.2442
Epoch 2/100
390/390 [==============================] - 11s 29ms/step - loss: 1.2757 - accuracy: 0.5581 - val_loss: 1.9118 - val_accuracy: 0.4016
Epoch 3/100
390/390 [==============================] - 11s 29ms/step - loss: 1.1264 - accuracy: 0.6105 - val_loss: 1.2568 - val_accuracy: 0.5498
Epoch 4/100
390/390 [==============================] - 11s 29ms/step - loss: 1.0150 - accuracy: 0.6484 - val_loss: 1.3001 - val_accuracy: 0.5564
Epoch 5/100
390/390 [==============================] - 11s 29ms/step - loss: 0.9265 - accuracy: 0.6820 - val_loss: 0.9689 - val_accuracy: 0.6802
Epoch 6/100
390/390 [==============================] - 11s 29ms/step - loss: 0.8634 - accuracy: 0.7041 - val_loss: 1.3286 - val_accuracy: 0.5624
Epoch 7/100
390/390 [==============================] - 11s 29ms/step - loss: 0.8024 - accuracy: 0.7261 - val_loss: 1.0214 - val_accuracy: 0.6676
Epoch 8/100
390/390 [==============================] - 11s 28ms/step - loss: 0.7602 - accuracy: 0.7416 - val_loss: 0.9298 - val_accuracy: 0.6804
Epoch 9/100
390/390 [==============================] - 11s 29ms/step - loss: 0.7220 - accuracy: 0.7575 - val_loss: 0.9745 - val_accuracy: 0.6784
Epoch 10/100
390/390 [==============================] - 11s 29ms/step - loss: 0.6771 - accuracy: 0.7717 - val_loss: 0.7908 - val_accuracy: 0.7244
Epoch 11/100
390/390 [==============================] - 11s 29ms/step - loss: 0.6520 - accuracy: 0.7819 - val_loss: 0.9043 - val_accuracy: 0.6934
Epoch 12/100
390/390 [==============================] - 11s 29ms/step - loss: 0.6146 - accuracy: 0.7947 - val_loss: 0.7787 - val_accuracy: 0.7446
Epoch 13/100
390/390 [==============================] - 11s 28ms/step - loss: 0.5933 - accuracy: 0.7997 - val_loss: 0.7505 - val_accuracy: 0.7528
Epoch 14/100
390/390 [==============================] - 11s 28ms/step - loss: 0.5710 - accuracy: 0.8072 - val_loss: 0.7057 - val_accuracy: 0.7644
Epoch 15/100
390/390 [==============================] - 11s 29ms/step - loss: 0.5457 - accuracy: 0.8174 - val_loss: 0.8235 - val_accuracy: 0.7312
Epoch 16/100
390/390 [==============================] - 11s 29ms/step - loss: 0.5243 - accuracy: 0.8262 - val_loss: 0.6631 - val_accuracy: 0.7672
Epoch 17/100
390/390 [==============================] - 11s 28ms/step - loss: 0.5073 - accuracy: 0.8307 - val_loss: 0.6935 - val_accuracy: 0.7734
Epoch 18/100
390/390 [==============================] - 11s 29ms/step - loss: 0.4879 - accuracy: 0.8380 - val_loss: 0.6700 - val_accuracy: 0.7748
Epoch 19/100
390/390 [==============================] - 11s 28ms/step - loss: 0.4698 - accuracy: 0.8414 - val_loss: 0.6904 - val_accuracy: 0.7714
Epoch 20/100
390/390 [==============================] - 11s 29ms/step - loss: 0.4508 - accuracy: 0.8479 - val_loss: 0.6207 - val_accuracy: 0.8012
Epoch 21/100
390/390 [==============================] - 12s 31ms/step - loss: 0.4416 - accuracy: 0.8536 - val_loss: 0.7352 - val_accuracy: 0.7684
Epoch 22/100
390/390 [==============================] - 11s 29ms/step - loss: 0.4235 - accuracy: 0.8586 - val_loss: 0.9226 - val_accuracy: 0.7102
Epoch 23/100
390/390 [==============================] - 11s 29ms/step - loss: 0.4118 - accuracy: 0.8635 - val_loss: 0.6872 - val_accuracy: 0.7864
Epoch 24/100
390/390 [==============================] - 11s 29ms/step - loss: 0.4054 - accuracy: 0.8633 - val_loss: 0.6349 - val_accuracy: 0.7980
Epoch 25/100
390/390 [==============================] - 11s 29ms/step - loss: 0.3873 - accuracy: 0.8716 - val_loss: 0.5795 - val_accuracy: 0.8112
Epoch 26/100
390/390 [==============================] - 11s 29ms/step - loss: 0.3743 - accuracy: 0.8762 - val_loss: 0.7991 - val_accuracy: 0.7580
Epoch 27/100
390/390 [==============================] - 11s 28ms/step - loss: 0.3623 - accuracy: 0.8811 - val_loss: 0.6132 - val_accuracy: 0.8048
Epoch 28/100
390/390 [==============================] - 11s 28ms/step - loss: 0.3537 - accuracy: 0.8821 - val_loss: 0.6159 - val_accuracy: 0.8034
Epoch 29/100
390/390 [==============================] - 11s 29ms/step - loss: 0.3451 - accuracy: 0.8871 - val_loss: 0.5890 - val_accuracy: 0.8172
Epoch 30/100
390/390 [==============================] - 11s 29ms/step - loss: 0.3341 - accuracy: 0.8888 - val_loss: 0.5170 - val_accuracy: 0.8312
Epoch 31/100
390/390 [==============================] - 11s 29ms/step - loss: 0.3217 - accuracy: 0.8930 - val_loss: 0.6331 - val_accuracy: 0.7992
Epoch 32/100
390/390 [==============================] - 11s 29ms/step - loss: 0.3161 - accuracy: 0.8941 - val_loss: 0.6867 - val_accuracy: 0.7958
Epoch 33/100
390/390 [==============================] - 11s 29ms/step - loss: 0.3062 - accuracy: 0.8975 - val_loss: 0.6233 - val_accuracy: 0.8064
Epoch 34/100
390/390 [==============================] - 11s 28ms/step - loss: 0.3011 - accuracy: 0.8990 - val_loss: 0.5072 - val_accuracy: 0.8326
Epoch 35/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2936 - accuracy: 0.9028 - val_loss: 0.5389 - val_accuracy: 0.8330
Epoch 36/100
390/390 [==============================] - 11s 28ms/step - loss: 0.2840 - accuracy: 0.9071 - val_loss: 0.6223 - val_accuracy: 0.8054
Epoch 37/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2734 - accuracy: 0.9085 - val_loss: 0.5799 - val_accuracy: 0.8176
Epoch 38/100
390/390 [==============================] - 11s 28ms/step - loss: 0.2720 - accuracy: 0.9092 - val_loss: 0.5513 - val_accuracy: 0.8324
Epoch 39/100
390/390 [==============================] - 11s 28ms/step - loss: 0.2625 - accuracy: 0.9120 - val_loss: 0.6154 - val_accuracy: 0.8116
Epoch 40/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2576 - accuracy: 0.9140 - val_loss: 0.5031 - val_accuracy: 0.8404
Epoch 41/100
390/390 [==============================] - 11s 28ms/step - loss: 0.2503 - accuracy: 0.9162 - val_loss: 0.5218 - val_accuracy: 0.8374
Epoch 42/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2441 - accuracy: 0.9186 - val_loss: 0.5802 - val_accuracy: 0.8216
Epoch 43/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2317 - accuracy: 0.9229 - val_loss: 0.5359 - val_accuracy: 0.8374
Epoch 44/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2345 - accuracy: 0.9208 - val_loss: 0.5335 - val_accuracy: 0.8386
Epoch 45/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2283 - accuracy: 0.9241 - val_loss: 0.4964 - val_accuracy: 0.8444
Epoch 46/100
390/390 [==============================] - 11s 28ms/step - loss: 0.2186 - accuracy: 0.9274 - val_loss: 0.6013 - val_accuracy: 0.8216
Epoch 47/100
390/390 [==============================] - 11s 28ms/step - loss: 0.2146 - accuracy: 0.9275 - val_loss: 0.5554 - val_accuracy: 0.8246
Epoch 48/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2064 - accuracy: 0.9315 - val_loss: 0.4062 - val_accuracy: 0.8742
Epoch 49/100
390/390 [==============================] - 11s 29ms/step - loss: 0.2040 - accuracy: 0.9327 - val_loss: 0.5200 - val_accuracy: 0.8416
Epoch 50/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1994 - accuracy: 0.9340 - val_loss: 0.4557 - val_accuracy: 0.8644
Epoch 51/100
390/390 [==============================] - 11s 29ms/step - loss: 0.1911 - accuracy: 0.9371 - val_loss: 0.5573 - val_accuracy: 0.8282
Epoch 52/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1883 - accuracy: 0.9366 - val_loss: 0.5349 - val_accuracy: 0.8404
Epoch 53/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1818 - accuracy: 0.9392 - val_loss: 0.6524 - val_accuracy: 0.8090
Epoch 54/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1822 - accuracy: 0.9387 - val_loss: 0.5043 - val_accuracy: 0.8514
Epoch 55/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1825 - accuracy: 0.9401 - val_loss: 0.5196 - val_accuracy: 0.8500
Epoch 56/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1696 - accuracy: 0.9433 - val_loss: 0.4863 - val_accuracy: 0.8546
Epoch 57/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1662 - accuracy: 0.9452 - val_loss: 0.6238 - val_accuracy: 0.8246
Epoch 58/100
390/390 [==============================] - 11s 28ms/step - loss: 0.1617 - accuracy: 0.9466 - val_loss: 0.5467 - val_accuracy: 0.8404
1/1 [==============================] - 2s 2s/step - loss: 0.5631 - accuracy: 0.8316
Saving history to 'history.pkl'. (epochs: 58)
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gesamter Code
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers \
import Conv2D, MaxPool2D, GlobalAvgPool2D, Dense, \
BatchNormalization, ReLU, Softmax
from tensorflow.keras.datasets.cifar10 import load_data
from radam import RAdam
from tensorflow.keras.utils import to_categorical
import pickle
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
os.environ['TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD'] = '10737418240'
def create_model():
model = Sequential([
Conv2D(filters=64, kernel_size=3, strides=1, padding='same', input_shape=(32,32,3)),
BatchNormalization(), ReLU(),
Conv2D(filters=64, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
Conv2D(filters=128, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=128, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
GlobalAvgPool2D(),
Dense(units=4096),
BatchNormalization(), ReLU(),
Dense(units=4096),
BatchNormalization(), ReLU(),
Dense(units=10),
BatchNormalization(), ReLU(),
Softmax()
])
return model
def data_augmentation(img):
im = tf.image.random_flip_left_right(img)
im = tf.image.random_flip_up_down(im)
im = tf.pad(im, tf.constant([[2, 2], [2, 2], [0, 0]]), "REFLECT")
im = tf.image.random_crop(im, size=[32, 32, 3])
return im
def train(batch_size, epochs, lr):
# Setup tpu
print("Setting up TPU ...")
tpu_grpc_url = "grpc://" + os.environ["COLAB_TPU_ADDR"]
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu_grpc_url)
tf.config.experimental_connect_to_cluster(tpu_cluster_resolver)
tf.tpu.experimental.initialize_tpu_system(tpu_cluster_resolver)
strategy = tf.distribute.experimental.TPUStrategy(tpu_cluster_resolver)
print("Done!")
# Load dataset
(x_train, y_train), (x_test, y_test) = load_data()
x_val, y_val = x_test[:5000], y_test[:5000]
x_test, y_test = x_test[5000:10000], y_test[5000:10000]
train_num = len(x_train)
val_num = len(x_val)
test_num = len(x_test)
# Normalize, convert to one-hot
x_train, y_train = x_train.astype(np.float32), y_train.astype(np.int32)
x_train /= 255.0
x_val, y_val = x_val.astype(np.float32), y_val.astype(np.int32)
x_val /= 255.0
x_test, y_test = x_test.astype(np.float32), y_test.astype(np.int32)
x_test /= 255.0
y_train = to_categorical(y_train, num_classes=10, dtype=np.int32)
y_val = to_categorical(y_val, num_classes=10, dtype=np.int32)
y_test = to_categorical(y_test, num_classes=10, dtype=np.int32)
# Convert to tf.data
trainset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
trainset = trainset.map(lambda image, label: (data_augmentation(image), label)).shuffle(buffer_size=1024).repeat().batch(batch_size)
valset = tf.data.Dataset.from_tensor_slices((x_val, y_val))
valset = valset.shuffle(buffer_size=1024).batch(val_num)
testset = tf.data.Dataset.from_tensor_slices((x_test, y_test))
testset = testset.batch(test_num)
# Train
callbacks = []
callbacks.append(tf.keras.callbacks.EarlyStopping('val_loss', patience=10))
op = RAdam(lr=lr)
with strategy.scope():
model = create_model()
model.compile(optimizer=op, loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(
trainset, epochs=epochs,
validation_data=valset, callbacks=callbacks,
steps_per_epoch=train_num // batch_size
)
model.evaluate(testset)
model.save('model2.h5')
# Save history
save_file = 'history2.pkl'
with open(save_file, 'wb') as f:
pickle.dump(history.history, f)
print(f"Saving history to \'{save_file}\'. (epochs: {len(history.epoch)})")
train(128, 100, 0.01)
import os
import numpy as np
import tensorflow as tf
from tensorflow.keras import Sequential
from tensorflow.keras.layers \
import Conv2D, MaxPool2D, GlobalAvgPool2D, Dense, \
BatchNormalization, ReLU, Softmax
from tensorflow.keras.datasets.cifar10 import load_data
from radam import RAdam
from tensorflow.keras.utils import to_categorical
import pickle
os.environ['TF_CPP_MIN_LOG_LEVEL']='2'
os.environ['TCMALLOC_LARGE_ALLOC_REPORT_THRESHOLD'] = '10737418240'
def create_model():
model = Sequential([
Conv2D(filters=64, kernel_size=3, strides=1, padding='same', input_shape=(32,32,3)),
BatchNormalization(), ReLU(),
Conv2D(filters=64, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
Conv2D(filters=128, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=128, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
GlobalAvgPool2D(),
Dense(units=4096),
BatchNormalization(), ReLU(),
Dense(units=4096),
BatchNormalization(), ReLU(),
Dense(units=10),
BatchNormalization(), ReLU(),
Softmax()
])
return model
def data_augmentation(img):
im = tf.image.random_flip_left_right(img)
im = tf.image.random_flip_up_down(im)
im = tf.pad(im, tf.constant([[2, 2], [2, 2], [0, 0]]), "REFLECT")
im = tf.image.random_crop(im, size=[32, 32, 3])
return im
def train(batch_size, epochs, lr):
# Setup tpu
print("Setting up TPU ...")
tpu_grpc_url = "grpc://" + os.environ["COLAB_TPU_ADDR"]
tpu_cluster_resolver = tf.distribute.cluster_resolver.TPUClusterResolver(tpu_grpc_url)
tf.config.experimental_connect_to_cluster(tpu_cluster_resolver)
tf.tpu.experimental.initialize_tpu_system(tpu_cluster_resolver)
strategy = tf.distribute.experimental.TPUStrategy(tpu_cluster_resolver)
print("Done!")
# Load dataset
(x_train, y_train), (x_test, y_test) = load_data()
x_val, y_val = x_test[:5000], y_test[:5000]
x_test, y_test = x_test[5000:10000], y_test[5000:10000]
train_num = len(x_train)
val_num = len(x_val)
test_num = len(x_test)
# Normalize, convert to one-hot
x_train, y_train = x_train.astype(np.float32), y_train.astype(np.int32)
x_train /= 255.0
x_val, y_val = x_val.astype(np.float32), y_val.astype(np.int32)
x_val /= 255.0
x_test, y_test = x_test.astype(np.float32), y_test.astype(np.int32)
x_test /= 255.0
y_train = to_categorical(y_train, num_classes=10, dtype=np.int32)
y_val = to_categorical(y_val, num_classes=10, dtype=np.int32)
y_test = to_categorical(y_test, num_classes=10, dtype=np.int32)
# Convert to tf.data
trainset = tf.data.Dataset.from_tensor_slices((x_train, y_train))
trainset = trainset.map(lambda image, label: (data_augmentation(image), label)).shuffle(buffer_size=1024).repeat().batch(batch_size)
valset = tf.data.Dataset.from_tensor_slices((x_val, y_val))
valset = valset.shuffle(buffer_size=1024).batch(val_num)
testset = tf.data.Dataset.from_tensor_slices((x_test, y_test))
testset = testset.batch(test_num)
# Train
callbacks = []
callbacks.append(tf.keras.callbacks.EarlyStopping('val_loss', patience=10))
op = RAdam(lr=lr)
with strategy.scope():
model = create_model()
model.compile(optimizer=op, loss='categorical_crossentropy', metrics=['accuracy'])
history = model.fit(
trainset, epochs=epochs,
validation_data=valset, callbacks=callbacks,
steps_per_epoch=train_num // batch_size
)
model.evaluate(testset)
model.save('model2.h5')
# Save history
save_file = 'history2.pkl'
with open(save_file, 'wb') as f:
pickle.dump(history.history, f)
print(f"Saving history to \'{save_file}\'. (epochs: {len(history.epoch)})")
train(128, 100, 0.01)
Machen Sie CNN komplizierter
model = Sequential([
Conv2D(filters=64, kernel_size=3, strides=1, padding='same', input_shape=(32,32,3)),
BatchNormalization(), ReLU(),
Conv2D(filters=64, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
Conv2D(filters=128, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=128, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=256, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
Conv2D(filters=512, kernel_size=3, strides=1, padding='same'),
BatchNormalization(), ReLU(),
MaxPool2D(pool_size=2, strides=2, padding='same'),
GlobalAvgPool2D(),
Dense(units=4096),
BatchNormalization(), ReLU(),
Dense(units=4096),
BatchNormalization(), ReLU(),
Dense(units=10),
BatchNormalization(), ReLU(),
Softmax()
])
Wir sind näher an der VGG16-Kopffamilie. Nun, es gibt andere ausgezeichnete wie Resnet und Xception, aber das ist okay.
Verwenden Sie den neuesten Optimierer
radam.py
class RAdam(Optimizer):
"""RAdam optimizer.
Default parameters follow those provided in the original Adam paper.
# Arguments
lr: float >= 0. Learning rate.\\
beta_1: float, 0 < beta < 1. Generally close to 1.\\
beta_2: float, 0 < beta < 1. Generally close to 1.\\
epsilon: float >= 0. Fuzz factor. If `None`, defaults to `K.epsilon()`.\\
decay: float >= 0. Learning rate decay over each update.\\
amsgrad: boolean. Whether to apply the AMSGrad variant of this
algorithm from the paper "On the Convergence of Adam and
Beyond".
# References
- [RAdam - A Method for Stochastic Optimization]
(https://arxiv.org/abs/1908.03265)
- [On The Variance Of The Adaptive Learning Rate And Beyond]
(https://arxiv.org/abs/1908.03265)
"""
def __init__(self,
learning_rate=0.001,
beta_1=0.9,
beta_2=0.999,
epsilon=None,
weight_decay=0.0,
name='RAdam', **kwargs):
super(RAdam, self).__init__(name, **kwargs)
self._set_hyper('learning_rate', kwargs.get('lr', learning_rate))
self._set_hyper('beta_1', beta_1)
self._set_hyper('beta_2', beta_2)
self._set_hyper('decay', self._initial_decay)
self.epsilon = epsilon or K.epsilon()
self.weight_decay = weight_decay
def _create_slots(self, var_list):
for var in var_list:
self.add_slot(var, 'm')
for var in var_list:
self.add_slot(var, 'v')
def _resource_apply_dense(self, grad, var):
var_dtype = var.dtype.base_dtype
lr_t = self._decayed_lr(var_dtype)
m = self.get_slot(var, 'm')
v = self.get_slot(var, 'v')
beta_1_t = self._get_hyper('beta_1', var_dtype)
beta_2_t = self._get_hyper('beta_2', var_dtype)
epsilon_t = tf.convert_to_tensor(self.epsilon, var_dtype)
t = tf.cast(self.iterations + 1, var_dtype)
m_t = (beta_1_t * m) + (1. - beta_1_t) * grad
v_t = (beta_2_t * v) + (1. - beta_2_t) * tf.square(grad)
beta2_t = beta_2_t ** t
N_sma_max = 2 / (1 - beta_2_t) - 1
N_sma = N_sma_max - 2 * t * beta2_t / (1 - beta2_t)
# apply weight decay
if self.weight_decay != 0.:
p_wd = var - self.weight_decay * lr_t * var
else:
p_wd = None
if p_wd is None:
p_ = var
else:
p_ = p_wd
def gt_path():
step_size = lr_t * tf.sqrt(
(1 - beta2_t) * (N_sma - 4) / (N_sma_max - 4) * (N_sma - 2) / N_sma * N_sma_max / (N_sma_max - 2)
) / (1 - beta_1_t ** t)
denom = tf.sqrt(v_t) + epsilon_t
p_t = p_ - step_size * (m_t / denom)
return p_t
def lt_path():
step_size = lr_t / (1 - beta_1_t ** t)
p_t = p_ - step_size * m_t
return p_t
p_t = tf.cond(N_sma > 5, gt_path, lt_path)
m_t = tf.compat.v1.assign(m, m_t)
v_t = tf.compat.v1.assign(v, v_t)
with tf.control_dependencies([m_t, v_t]):
param_update = tf.compat.v1.assign(var, p_t)
return tf.group(*[param_update, m_t, v_t])
def _resource_apply_sparse(self, grad, handle, indices):
raise NotImplementedError("Sparse data is not supported yet")
def get_config(self):
config = super(RAdam, self).get_config()
config.update({
'learning_rate': self._serialize_hyperparameter('learning_rate'),
'decay': self._serialize_hyperparameter('decay'),
'beta_1': self._serialize_hyperparameter('beta_1'),
'beta_2': self._serialize_hyperparameter('beta_2'),
'epsilon': self.epsilon,
'weight_decay': self.weight_decay,
})
return config
Der beliebte Adam ist gut, aber ich werde die verbesserte Version RAdam übernehmen. Grob gesagt ist es wie eine Kombination aus Moment SGD und Adam. Wenn Sie oben in der Zelle "%% writefile radam.py" hinzufügen und ausführen, wird die Datei überschrieben (oder neu erstellt). Einmal erstellt, können Sie mit "from radam import RAdam" darauf verweisen.
Lernen
Kompilieren und passen Sie auf die gleiche Weise.
Ausführungsergebnis
Setting up TPU ...
INFO:tensorflow:Initializing the TPU system: 10.111.159.218:8470
INFO:tensorflow:Initializing the TPU system: 10.111.159.218:8470
INFO:tensorflow:Clearing out eager caches
INFO:tensorflow:Clearing out eager caches
INFO:tensorflow:Finished initializing TPU system.
INFO:tensorflow:Finished initializing TPU system.
INFO:tensorflow:Found TPU system:
INFO:tensorflow:Found TPU system:
INFO:tensorflow:*** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
Done!
Train on 390 steps, validate on 1 steps
Epoch 1/100
390/390 [==============================] - 59s 151ms/step - loss: 1.8134 - accuracy: 0.3453 - val_loss: 5.4962 - val_accuracy: 0.2774
Epoch 2/100
390/390 [==============================] - 15s 40ms/step - loss: 1.5007 - accuracy: 0.4666 - val_loss: 4.3148 - val_accuracy: 0.3578
Epoch 3/100
390/390 [==============================] - 16s 40ms/step - loss: 1.3087 - accuracy: 0.5373 - val_loss: 2.2541 - val_accuracy: 0.4854
Epoch 4/100
390/390 [==============================] - 15s 40ms/step - loss: 1.1609 - accuracy: 0.5954 - val_loss: 1.6294 - val_accuracy: 0.5296
Epoch 5/100
390/390 [==============================] - 16s 41ms/step - loss: 1.0613 - accuracy: 0.6346 - val_loss: 1.2080 - val_accuracy: 0.6104
Epoch 6/100
390/390 [==============================] - 16s 40ms/step - loss: 0.9717 - accuracy: 0.6696 - val_loss: 1.3208 - val_accuracy: 0.5942
Epoch 7/100
390/390 [==============================] - 16s 40ms/step - loss: 0.9017 - accuracy: 0.6955 - val_loss: 1.3902 - val_accuracy: 0.5940
Epoch 8/100
390/390 [==============================] - 16s 40ms/step - loss: 0.8464 - accuracy: 0.7188 - val_loss: 0.9953 - val_accuracy: 0.6920
Epoch 9/100
390/390 [==============================] - 16s 40ms/step - loss: 0.7957 - accuracy: 0.7360 - val_loss: 1.1091 - val_accuracy: 0.6478
Epoch 10/100
390/390 [==============================] - 16s 40ms/step - loss: 0.7436 - accuracy: 0.7546 - val_loss: 0.8850 - val_accuracy: 0.7030
Epoch 11/100
390/390 [==============================] - 16s 40ms/step - loss: 0.7095 - accuracy: 0.7634 - val_loss: 0.7887 - val_accuracy: 0.7370
Epoch 12/100
390/390 [==============================] - 16s 40ms/step - loss: 0.6654 - accuracy: 0.7831 - val_loss: 0.8152 - val_accuracy: 0.7310
Epoch 13/100
390/390 [==============================] - 16s 41ms/step - loss: 0.6306 - accuracy: 0.7942 - val_loss: 0.9708 - val_accuracy: 0.6908
Epoch 14/100
390/390 [==============================] - 16s 40ms/step - loss: 0.6042 - accuracy: 0.8034 - val_loss: 0.9384 - val_accuracy: 0.6928
Epoch 15/100
390/390 [==============================] - 16s 40ms/step - loss: 0.5818 - accuracy: 0.8109 - val_loss: 0.6889 - val_accuracy: 0.7816
Epoch 16/100
390/390 [==============================] - 15s 40ms/step - loss: 0.5588 - accuracy: 0.8199 - val_loss: 0.7431 - val_accuracy: 0.7612
Epoch 17/100
390/390 [==============================] - 16s 40ms/step - loss: 0.5267 - accuracy: 0.8310 - val_loss: 0.8451 - val_accuracy: 0.7414
Epoch 18/100
390/390 [==============================] - 16s 41ms/step - loss: 0.5040 - accuracy: 0.8382 - val_loss: 0.9923 - val_accuracy: 0.6854
Epoch 19/100
390/390 [==============================] - 16s 40ms/step - loss: 0.4935 - accuracy: 0.8424 - val_loss: 0.6350 - val_accuracy: 0.7880
Epoch 20/100
390/390 [==============================] - 16s 40ms/step - loss: 0.4707 - accuracy: 0.8472 - val_loss: 0.5511 - val_accuracy: 0.8188
Epoch 21/100
390/390 [==============================] - 16s 40ms/step - loss: 0.4546 - accuracy: 0.8539 - val_loss: 0.5996 - val_accuracy: 0.8056
Epoch 22/100
390/390 [==============================] - 16s 40ms/step - loss: 0.4357 - accuracy: 0.8615 - val_loss: 0.6641 - val_accuracy: 0.7864
Epoch 23/100
390/390 [==============================] - 16s 40ms/step - loss: 0.4259 - accuracy: 0.8637 - val_loss: 0.5961 - val_accuracy: 0.8134
Epoch 24/100
390/390 [==============================] - 16s 40ms/step - loss: 0.4054 - accuracy: 0.8689 - val_loss: 0.5884 - val_accuracy: 0.8190
Epoch 25/100
390/390 [==============================] - 16s 41ms/step - loss: 0.3913 - accuracy: 0.8750 - val_loss: 0.5741 - val_accuracy: 0.8238
Epoch 26/100
390/390 [==============================] - 16s 41ms/step - loss: 0.3784 - accuracy: 0.8774 - val_loss: 0.5228 - val_accuracy: 0.8368
Epoch 27/100
390/390 [==============================] - 16s 40ms/step - loss: 0.3645 - accuracy: 0.8838 - val_loss: 0.6014 - val_accuracy: 0.8166
Epoch 28/100
390/390 [==============================] - 16s 40ms/step - loss: 0.3498 - accuracy: 0.8893 - val_loss: 0.5302 - val_accuracy: 0.8298
Epoch 29/100
390/390 [==============================] - 16s 40ms/step - loss: 0.3323 - accuracy: 0.8917 - val_loss: 0.6008 - val_accuracy: 0.8098
Epoch 30/100
390/390 [==============================] - 16s 40ms/step - loss: 0.3362 - accuracy: 0.8923 - val_loss: 0.5984 - val_accuracy: 0.8048
Epoch 31/100
390/390 [==============================] - 16s 40ms/step - loss: 0.3225 - accuracy: 0.8971 - val_loss: 0.4980 - val_accuracy: 0.8364
Epoch 32/100
390/390 [==============================] - 16s 40ms/step - loss: 0.3095 - accuracy: 0.9016 - val_loss: 0.4806 - val_accuracy: 0.8426
Epoch 33/100
390/390 [==============================] - 16s 41ms/step - loss: 0.3046 - accuracy: 0.9034 - val_loss: 0.4721 - val_accuracy: 0.8520
Epoch 34/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2891 - accuracy: 0.9079 - val_loss: 0.4913 - val_accuracy: 0.8418
Epoch 35/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2826 - accuracy: 0.9099 - val_loss: 0.5302 - val_accuracy: 0.8274
Epoch 36/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2779 - accuracy: 0.9103 - val_loss: 0.6216 - val_accuracy: 0.8034
Epoch 37/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2630 - accuracy: 0.9176 - val_loss: 0.4146 - val_accuracy: 0.8638
Epoch 38/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2558 - accuracy: 0.9187 - val_loss: 0.4989 - val_accuracy: 0.8408
Epoch 39/100
390/390 [==============================] - 16s 41ms/step - loss: 0.2507 - accuracy: 0.9185 - val_loss: 0.5337 - val_accuracy: 0.8370
Epoch 40/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2417 - accuracy: 0.9237 - val_loss: 0.4495 - val_accuracy: 0.8574
Epoch 41/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2313 - accuracy: 0.9254 - val_loss: 0.5956 - val_accuracy: 0.8180
Epoch 42/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2328 - accuracy: 0.9250 - val_loss: 0.4339 - val_accuracy: 0.8634
Epoch 43/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2285 - accuracy: 0.9263 - val_loss: 0.3959 - val_accuracy: 0.8734
Epoch 44/100
390/390 [==============================] - 16s 41ms/step - loss: 0.2123 - accuracy: 0.9323 - val_loss: 0.4857 - val_accuracy: 0.8392
Epoch 45/100
390/390 [==============================] - 16s 41ms/step - loss: 0.2045 - accuracy: 0.9331 - val_loss: 0.4265 - val_accuracy: 0.8634
Epoch 46/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2062 - accuracy: 0.9326 - val_loss: 0.5432 - val_accuracy: 0.8358
Epoch 47/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1986 - accuracy: 0.9359 - val_loss: 0.5131 - val_accuracy: 0.8394
Epoch 48/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2002 - accuracy: 0.9362 - val_loss: 0.3935 - val_accuracy: 0.8746
Epoch 49/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1851 - accuracy: 0.9389 - val_loss: 0.5090 - val_accuracy: 0.8490
Epoch 50/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1819 - accuracy: 0.9405 - val_loss: 0.5670 - val_accuracy: 0.8300
Epoch 51/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1794 - accuracy: 0.9417 - val_loss: 0.4944 - val_accuracy: 0.8570
Epoch 52/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1784 - accuracy: 0.9406 - val_loss: 0.4221 - val_accuracy: 0.8712
Epoch 53/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1666 - accuracy: 0.9455 - val_loss: 0.4772 - val_accuracy: 0.8522
Epoch 54/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1657 - accuracy: 0.9454 - val_loss: 0.3937 - val_accuracy: 0.8782
Epoch 55/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1663 - accuracy: 0.9463 - val_loss: 0.4190 - val_accuracy: 0.8710
Epoch 56/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1589 - accuracy: 0.9485 - val_loss: 0.3726 - val_accuracy: 0.8840
Epoch 57/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1583 - accuracy: 0.9474 - val_loss: 0.4746 - val_accuracy: 0.8538
Epoch 58/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1526 - accuracy: 0.9502 - val_loss: 0.5419 - val_accuracy: 0.8432
Epoch 59/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1543 - accuracy: 0.9506 - val_loss: 0.3782 - val_accuracy: 0.8840
Epoch 60/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1448 - accuracy: 0.9526 - val_loss: 0.4636 - val_accuracy: 0.8556
Epoch 61/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1404 - accuracy: 0.9545 - val_loss: 0.4362 - val_accuracy: 0.8754
Epoch 62/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1391 - accuracy: 0.9543 - val_loss: 0.5420 - val_accuracy: 0.8528
Epoch 63/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1409 - accuracy: 0.9531 - val_loss: 0.3796 - val_accuracy: 0.8848
Epoch 64/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1328 - accuracy: 0.9563 - val_loss: 0.5381 - val_accuracy: 0.8344
Epoch 65/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1347 - accuracy: 0.9562 - val_loss: 0.4141 - val_accuracy: 0.8744
Epoch 66/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1289 - accuracy: 0.9577 - val_loss: 0.3722 - val_accuracy: 0.8888
Epoch 67/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1263 - accuracy: 0.9587 - val_loss: 0.4023 - val_accuracy: 0.8812
Epoch 68/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1181 - accuracy: 0.9612 - val_loss: 0.4208 - val_accuracy: 0.8778
Epoch 69/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1216 - accuracy: 0.9598 - val_loss: 0.4888 - val_accuracy: 0.8556
Epoch 70/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1160 - accuracy: 0.9625 - val_loss: 0.6075 - val_accuracy: 0.8378
Epoch 71/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1214 - accuracy: 0.9598 - val_loss: 0.4046 - val_accuracy: 0.8742
Epoch 72/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1121 - accuracy: 0.9627 - val_loss: 0.4819 - val_accuracy: 0.8662
Epoch 73/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1128 - accuracy: 0.9629 - val_loss: 0.4114 - val_accuracy: 0.8804
Epoch 74/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1101 - accuracy: 0.9646 - val_loss: 0.4471 - val_accuracy: 0.8756
Epoch 75/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1040 - accuracy: 0.9655 - val_loss: 0.4560 - val_accuracy: 0.8752
Epoch 76/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1099 - accuracy: 0.9641 - val_loss: 0.4584 - val_accuracy: 0.8724
1/1 [==============================] - 2s 2s/step - loss: 0.4376 - accuracy: 0.8698
Saving history to 'history2.pkl'. (epochs: 76)
Setting up TPU ...
INFO:tensorflow:Initializing the TPU system: 10.111.159.218:8470
INFO:tensorflow:Initializing the TPU system: 10.111.159.218:8470
INFO:tensorflow:Clearing out eager caches
INFO:tensorflow:Clearing out eager caches
INFO:tensorflow:Finished initializing TPU system.
INFO:tensorflow:Finished initializing TPU system.
INFO:tensorflow:Found TPU system:
INFO:tensorflow:Found TPU system:
INFO:tensorflow:*** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Cores: 8
INFO:tensorflow:*** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Workers: 1
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Num TPU Cores Per Worker: 8
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:localhost/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:CPU:0, CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:0, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:1, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:2, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:3, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:4, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:5, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:6, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU:7, TPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:TPU_SYSTEM:0, TPU_SYSTEM, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
INFO:tensorflow:*** Available Device: _DeviceAttributes(/job:worker/replica:0/task:0/device:XLA_CPU:0, XLA_CPU, 0, 0)
Done!
Train on 390 steps, validate on 1 steps
Epoch 1/100
390/390 [==============================] - 59s 151ms/step - loss: 1.8134 - accuracy: 0.3453 - val_loss: 5.4962 - val_accuracy: 0.2774
Epoch 2/100
390/390 [==============================] - 15s 40ms/step - loss: 1.5007 - accuracy: 0.4666 - val_loss: 4.3148 - val_accuracy: 0.3578
Epoch 3/100
390/390 [==============================] - 16s 40ms/step - loss: 1.3087 - accuracy: 0.5373 - val_loss: 2.2541 - val_accuracy: 0.4854
Epoch 4/100
390/390 [==============================] - 15s 40ms/step - loss: 1.1609 - accuracy: 0.5954 - val_loss: 1.6294 - val_accuracy: 0.5296
Epoch 5/100
390/390 [==============================] - 16s 41ms/step - loss: 1.0613 - accuracy: 0.6346 - val_loss: 1.2080 - val_accuracy: 0.6104
Epoch 6/100
390/390 [==============================] - 16s 40ms/step - loss: 0.9717 - accuracy: 0.6696 - val_loss: 1.3208 - val_accuracy: 0.5942
Epoch 7/100
390/390 [==============================] - 16s 40ms/step - loss: 0.9017 - accuracy: 0.6955 - val_loss: 1.3902 - val_accuracy: 0.5940
Epoch 8/100
390/390 [==============================] - 16s 40ms/step - loss: 0.8464 - accuracy: 0.7188 - val_loss: 0.9953 - val_accuracy: 0.6920
Epoch 9/100
390/390 [==============================] - 16s 40ms/step - loss: 0.7957 - accuracy: 0.7360 - val_loss: 1.1091 - val_accuracy: 0.6478
Epoch 10/100
390/390 [==============================] - 16s 40ms/step - loss: 0.7436 - accuracy: 0.7546 - val_loss: 0.8850 - val_accuracy: 0.7030
Epoch 11/100
390/390 [==============================] - 16s 40ms/step - loss: 0.7095 - accuracy: 0.7634 - val_loss: 0.7887 - val_accuracy: 0.7370
Epoch 12/100
390/390 [==============================] - 16s 40ms/step - loss: 0.6654 - accuracy: 0.7831 - val_loss: 0.8152 - val_accuracy: 0.7310
Epoch 13/100
390/390 [==============================] - 16s 41ms/step - loss: 0.6306 - accuracy: 0.7942 - val_loss: 0.9708 - val_accuracy: 0.6908
Epoch 14/100
390/390 [==============================] - 16s 40ms/step - loss: 0.6042 - accuracy: 0.8034 - val_loss: 0.9384 - val_accuracy: 0.6928
Epoch 15/100
390/390 [==============================] - 16s 40ms/step - loss: 0.5818 - accuracy: 0.8109 - val_loss: 0.6889 - val_accuracy: 0.7816
Epoch 16/100
390/390 [==============================] - 15s 40ms/step - loss: 0.5588 - accuracy: 0.8199 - val_loss: 0.7431 - val_accuracy: 0.7612
Epoch 17/100
390/390 [==============================] - 16s 40ms/step - loss: 0.5267 - accuracy: 0.8310 - val_loss: 0.8451 - val_accuracy: 0.7414
Epoch 18/100
390/390 [==============================] - 16s 41ms/step - loss: 0.5040 - accuracy: 0.8382 - val_loss: 0.9923 - val_accuracy: 0.6854
Epoch 19/100
390/390 [==============================] - 16s 40ms/step - loss: 0.4935 - accuracy: 0.8424 - val_loss: 0.6350 - val_accuracy: 0.7880
Epoch 20/100
390/390 [==============================] - 16s 40ms/step - loss: 0.4707 - accuracy: 0.8472 - val_loss: 0.5511 - val_accuracy: 0.8188
Epoch 21/100
390/390 [==============================] - 16s 40ms/step - loss: 0.4546 - accuracy: 0.8539 - val_loss: 0.5996 - val_accuracy: 0.8056
Epoch 22/100
390/390 [==============================] - 16s 40ms/step - loss: 0.4357 - accuracy: 0.8615 - val_loss: 0.6641 - val_accuracy: 0.7864
Epoch 23/100
390/390 [==============================] - 16s 40ms/step - loss: 0.4259 - accuracy: 0.8637 - val_loss: 0.5961 - val_accuracy: 0.8134
Epoch 24/100
390/390 [==============================] - 16s 40ms/step - loss: 0.4054 - accuracy: 0.8689 - val_loss: 0.5884 - val_accuracy: 0.8190
Epoch 25/100
390/390 [==============================] - 16s 41ms/step - loss: 0.3913 - accuracy: 0.8750 - val_loss: 0.5741 - val_accuracy: 0.8238
Epoch 26/100
390/390 [==============================] - 16s 41ms/step - loss: 0.3784 - accuracy: 0.8774 - val_loss: 0.5228 - val_accuracy: 0.8368
Epoch 27/100
390/390 [==============================] - 16s 40ms/step - loss: 0.3645 - accuracy: 0.8838 - val_loss: 0.6014 - val_accuracy: 0.8166
Epoch 28/100
390/390 [==============================] - 16s 40ms/step - loss: 0.3498 - accuracy: 0.8893 - val_loss: 0.5302 - val_accuracy: 0.8298
Epoch 29/100
390/390 [==============================] - 16s 40ms/step - loss: 0.3323 - accuracy: 0.8917 - val_loss: 0.6008 - val_accuracy: 0.8098
Epoch 30/100
390/390 [==============================] - 16s 40ms/step - loss: 0.3362 - accuracy: 0.8923 - val_loss: 0.5984 - val_accuracy: 0.8048
Epoch 31/100
390/390 [==============================] - 16s 40ms/step - loss: 0.3225 - accuracy: 0.8971 - val_loss: 0.4980 - val_accuracy: 0.8364
Epoch 32/100
390/390 [==============================] - 16s 40ms/step - loss: 0.3095 - accuracy: 0.9016 - val_loss: 0.4806 - val_accuracy: 0.8426
Epoch 33/100
390/390 [==============================] - 16s 41ms/step - loss: 0.3046 - accuracy: 0.9034 - val_loss: 0.4721 - val_accuracy: 0.8520
Epoch 34/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2891 - accuracy: 0.9079 - val_loss: 0.4913 - val_accuracy: 0.8418
Epoch 35/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2826 - accuracy: 0.9099 - val_loss: 0.5302 - val_accuracy: 0.8274
Epoch 36/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2779 - accuracy: 0.9103 - val_loss: 0.6216 - val_accuracy: 0.8034
Epoch 37/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2630 - accuracy: 0.9176 - val_loss: 0.4146 - val_accuracy: 0.8638
Epoch 38/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2558 - accuracy: 0.9187 - val_loss: 0.4989 - val_accuracy: 0.8408
Epoch 39/100
390/390 [==============================] - 16s 41ms/step - loss: 0.2507 - accuracy: 0.9185 - val_loss: 0.5337 - val_accuracy: 0.8370
Epoch 40/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2417 - accuracy: 0.9237 - val_loss: 0.4495 - val_accuracy: 0.8574
Epoch 41/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2313 - accuracy: 0.9254 - val_loss: 0.5956 - val_accuracy: 0.8180
Epoch 42/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2328 - accuracy: 0.9250 - val_loss: 0.4339 - val_accuracy: 0.8634
Epoch 43/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2285 - accuracy: 0.9263 - val_loss: 0.3959 - val_accuracy: 0.8734
Epoch 44/100
390/390 [==============================] - 16s 41ms/step - loss: 0.2123 - accuracy: 0.9323 - val_loss: 0.4857 - val_accuracy: 0.8392
Epoch 45/100
390/390 [==============================] - 16s 41ms/step - loss: 0.2045 - accuracy: 0.9331 - val_loss: 0.4265 - val_accuracy: 0.8634
Epoch 46/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2062 - accuracy: 0.9326 - val_loss: 0.5432 - val_accuracy: 0.8358
Epoch 47/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1986 - accuracy: 0.9359 - val_loss: 0.5131 - val_accuracy: 0.8394
Epoch 48/100
390/390 [==============================] - 16s 40ms/step - loss: 0.2002 - accuracy: 0.9362 - val_loss: 0.3935 - val_accuracy: 0.8746
Epoch 49/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1851 - accuracy: 0.9389 - val_loss: 0.5090 - val_accuracy: 0.8490
Epoch 50/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1819 - accuracy: 0.9405 - val_loss: 0.5670 - val_accuracy: 0.8300
Epoch 51/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1794 - accuracy: 0.9417 - val_loss: 0.4944 - val_accuracy: 0.8570
Epoch 52/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1784 - accuracy: 0.9406 - val_loss: 0.4221 - val_accuracy: 0.8712
Epoch 53/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1666 - accuracy: 0.9455 - val_loss: 0.4772 - val_accuracy: 0.8522
Epoch 54/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1657 - accuracy: 0.9454 - val_loss: 0.3937 - val_accuracy: 0.8782
Epoch 55/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1663 - accuracy: 0.9463 - val_loss: 0.4190 - val_accuracy: 0.8710
Epoch 56/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1589 - accuracy: 0.9485 - val_loss: 0.3726 - val_accuracy: 0.8840
Epoch 57/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1583 - accuracy: 0.9474 - val_loss: 0.4746 - val_accuracy: 0.8538
Epoch 58/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1526 - accuracy: 0.9502 - val_loss: 0.5419 - val_accuracy: 0.8432
Epoch 59/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1543 - accuracy: 0.9506 - val_loss: 0.3782 - val_accuracy: 0.8840
Epoch 60/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1448 - accuracy: 0.9526 - val_loss: 0.4636 - val_accuracy: 0.8556
Epoch 61/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1404 - accuracy: 0.9545 - val_loss: 0.4362 - val_accuracy: 0.8754
Epoch 62/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1391 - accuracy: 0.9543 - val_loss: 0.5420 - val_accuracy: 0.8528
Epoch 63/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1409 - accuracy: 0.9531 - val_loss: 0.3796 - val_accuracy: 0.8848
Epoch 64/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1328 - accuracy: 0.9563 - val_loss: 0.5381 - val_accuracy: 0.8344
Epoch 65/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1347 - accuracy: 0.9562 - val_loss: 0.4141 - val_accuracy: 0.8744
Epoch 66/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1289 - accuracy: 0.9577 - val_loss: 0.3722 - val_accuracy: 0.8888
Epoch 67/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1263 - accuracy: 0.9587 - val_loss: 0.4023 - val_accuracy: 0.8812
Epoch 68/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1181 - accuracy: 0.9612 - val_loss: 0.4208 - val_accuracy: 0.8778
Epoch 69/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1216 - accuracy: 0.9598 - val_loss: 0.4888 - val_accuracy: 0.8556
Epoch 70/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1160 - accuracy: 0.9625 - val_loss: 0.6075 - val_accuracy: 0.8378
Epoch 71/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1214 - accuracy: 0.9598 - val_loss: 0.4046 - val_accuracy: 0.8742
Epoch 72/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1121 - accuracy: 0.9627 - val_loss: 0.4819 - val_accuracy: 0.8662
Epoch 73/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1128 - accuracy: 0.9629 - val_loss: 0.4114 - val_accuracy: 0.8804
Epoch 74/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1101 - accuracy: 0.9646 - val_loss: 0.4471 - val_accuracy: 0.8756
Epoch 75/100
390/390 [==============================] - 16s 41ms/step - loss: 0.1040 - accuracy: 0.9655 - val_loss: 0.4560 - val_accuracy: 0.8752
Epoch 76/100
390/390 [==============================] - 16s 40ms/step - loss: 0.1099 - accuracy: 0.9641 - val_loss: 0.4584 - val_accuracy: 0.8724
1/1 [==============================] - 2s 2s/step - loss: 0.4376 - accuracy: 0.8698
Saving history to 'history2.pkl'. (epochs: 76)
Es war 86,98%. Es ändert sich nicht viel. Das war's. In Wirklichkeit ist es schwierig, ein paar Prozent zu erhöhen, daher denke ich, dass es ein gutes Ergebnis ist.
Lassen Sie uns das Modell visuell bewerten
Lassen Sie uns das trainierte Modell visuell bewerten. Dieses Mal werden wir eine Heatmap verwenden. Versuchen wir es mit dem RAdam-Modell, das die beste Genauigkeit aufweist.
gesamter Code
from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from tensorflow.keras.datasets.cifar10 import load_data
from tensorflow.keras.models import load_model
def plot_cmx(y_true, y_pred, labels):
cmx_data = confusion_matrix(y_true, y_pred)
df_cmx = pd.DataFrame(cmx_data, index=labels, columns=labels)
fig = plt.figure(figsize=(12.8, 9.6))
sns.heatmap(df_cmx, annot=True, fmt='.0f', linecolor='white', linewidths=1)
plt.xlabel("Predict")
plt.ylabel("True")
plt.show()
def eval():
# Set Labels
labels = [
'Airplane', 'Automobile', 'Bird', 'Cat', 'Deer',
'Dog', 'Frog', 'Horse', 'Ship', 'Truck'
]
# Load dataset
(_, _), (x_test, y_test) = load_data()
x_test, y_test = x_test[5000:10000], y_test[5000:10000]
# Normalize, flatten
x_test, y_test = x_test.astype(np.float32), y_test.astype(np.int32)
x_test /= 255.0
y_test = y_test.flatten()
model = load_model('model2.h5')
y_pred = model.predict_classes(x_test, batch_size=128)
print(classification_report(y_test, y_pred, target_names=labels))
plot_cmx(y_test, y_pred, labels)
eval()
from sklearn.metrics import confusion_matrix, classification_report
import matplotlib.pyplot as plt
import seaborn as sns
import pandas as pd
import numpy as np
from tensorflow.keras.datasets.cifar10 import load_data
from tensorflow.keras.models import load_model
def plot_cmx(y_true, y_pred, labels):
cmx_data = confusion_matrix(y_true, y_pred)
df_cmx = pd.DataFrame(cmx_data, index=labels, columns=labels)
fig = plt.figure(figsize=(12.8, 9.6))
sns.heatmap(df_cmx, annot=True, fmt='.0f', linecolor='white', linewidths=1)
plt.xlabel("Predict")
plt.ylabel("True")
plt.show()
def eval():
# Set Labels
labels = [
'Airplane', 'Automobile', 'Bird', 'Cat', 'Deer',
'Dog', 'Frog', 'Horse', 'Ship', 'Truck'
]
# Load dataset
(_, _), (x_test, y_test) = load_data()
x_test, y_test = x_test[5000:10000], y_test[5000:10000]
# Normalize, flatten
x_test, y_test = x_test.astype(np.float32), y_test.astype(np.int32)
x_test /= 255.0
y_test = y_test.flatten()
model = load_model('model2.h5')
y_pred = model.predict_classes(x_test, batch_size=128)
print(classification_report(y_test, y_pred, target_names=labels))
plot_cmx(y_test, y_pred, labels)
eval()
Machen Sie eine Heatmap
cmx_data = confusion_matrix(y_true, y_pred)
df_cmx = pd.DataFrame(cmx_data, index=labels, columns=labels)
fig = plt.figure(figsize=(12.8, 9.6))
sns.heatmap(df_cmx, annot=True, fmt='.0f', linecolor='white', linewidths=1)
plt.xlabel("Predict")
plt.ylabel("True")
plt.show()
Erstellen Sie einfach eine Verwirrungsmatrix und zeichnen Sie sie auf. In Google Colaboratory. Grafiken und Bilder können inline mit plt.show () angezeigt werden.
Prozessdaten
# Load dataset
(_, _), (x_test, y_test) = load_data()
x_test, y_test = x_test[5000:10000], y_test[5000:10000]
# Normalize, convert to one-hot
x_test, y_test = x_test.astype(np.float32), y_test.astype(np.int32)
x_test /= 255.0
y_test = y_test.flatten()
Ich habe einen heißen Ausdruck zum Lernen verwendet, aber da confusion_matrix verwendet wird, muss er geglättet werden. Mit anderen Worten, es ist eine Liste von Beschriftungen (eindimensionaler Vektor). Sie können flatten () verwenden.
Da die Anzahl der Testdaten 5000 betrug, werden wir sie vereinheitlichen.
Bewerten Sie das Modell
model = load_model('model2.h5')
y_pred = model.predict_classes(x_test, batch_size=128)
print(classification_report(y_test, y_pred, target_names=labels))
plot_cmx(y_test, y_pred, labels)
Extrahieren Sie den Guess-Label-Vektor y_pred mit Predict_classes. Durch Vergleichen mit dem Vektor y_test des richtigen Antwortetiketts kann die F1-Bewertung usw. erhalten werden.
Lassen Sie uns nun die Heatmap anzeigen. In dem diesmal hergestellten Modell wurde die folgende Figur erhalten.
Es scheint schwierig zu sein, zwischen Hunden und Katzen zu unterscheiden. Danach scheint es einige Fälle zu geben, in denen es als Katze beurteilt wird, obwohl es sich um einen Vogel handelt. Andere sehen im Allgemeinen gut aus.
Übrigens sind die F1-Punkte usw. wie folgt.
precision recall f1-score support
Airplane 0.94 0.86 0.90 512
Automobile 0.97 0.92 0.95 495
Bird 0.93 0.75 0.83 488
Cat 0.70 0.75 0.72 503
Deer 0.81 0.90 0.85 493
Dog 0.74 0.85 0.79 512
Frog 0.92 0.93 0.92 509
Horse 0.92 0.87 0.90 505
Ship 0.94 0.90 0.92 496
Truck 0.88 0.96 0.92 487
accuracy 0.87 5000
macro avg 0.88 0.87 0.87 5000
weighted avg 0.88 0.87 0.87 5000
Unterstützung ist die Bevölkerung. Die Unterscheidung zwischen Katzen und Hunden scheint ein Problem zu sein.
Zusammenfassung
Deep Learning wurde mit TPU durchgeführt. Jeder, bitte übernehmen Sie Resnet oder andere neuronale Netze oder ändern Sie die Anzahl der Epochen, des Optimierers usw., um die Genauigkeit zu verbessern.
Referenzseite
- On the Variance of the Adaptive Learning Rate and Beyond
- TensorFlow2.0 mit Keras für verschiedene MNIST (TPU-kompatibel)
- Ich habe Ramen ernsthaft klassifiziert
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