転移学習の学習が終わらない！

Question

前提・実現したいこと

学習をGoogle Colaboratory上で実行したい！
そのために考えうる,発生してる問題の解決策(一般論でも構いません！)をいくつかご教授頂ければ幸いです.

発生している問題・エラーメッセージ

epochが1/30から30分以上経っても進みません

該当のソースコード

import tensorflow as tf
import numpy as np
input_tensor = tf.keras.Input(shape=(224, 224, 3))
model=tf.keras.applications.VGG19(include_top=False,
                                    weights='imagenet',input_tensor=input_tensor)
x = model.output
x = tf.keras.layers.GlobalAveragePooling2D()(x)
x = tf.keras.layers.Dense(1024, activation='relu')(x)
predictions = tf.keras.layers.Dense(2, activation='softmax')(x)
model = tf.keras.Model(inputs=model.input, outputs=predictions)
for layer in model.layers:
   layer.trainable = False
model.compile(tf.keras.optimizers.SGD(lr=0.0001, momentum=0.9), loss='categorical_crossentropy',metrics=['accuracy'])

model.summary()

datagen = tf.keras.preprocessing.image.ImageDataGenerator()
train_datagen = tf.keras.preprocessing.image.ImageDataGenerator(
    'train_dir',
    shear_range=0.2,
    zoom_range=0.2,
    horizontal_flip=True,
    rotation_range=10)

test_datagen = tf.keras.preprocessing.image.ImageDataGenerator()

validation_generator = test_datagen.flow_from_directory(
   directory=test_dir,
   target_size=(224, 224),
   batch_size=16,
   class_mode='binary',
   shuffle=True)

train_generator = train_datagen.flow_from_directory(
   directory=train_dir,
   target_size=(224, 224),
   batch_size=16,
   class_mode='binary',
   shuffle=True
)

hist = model.fit_generator(train_generator,
    steps_per_epoch=1000,
    epochs=30,
    verbose=1,
    validation_data=validation_generator,
    validation_steps=250
    )

input_1 (InputLayer)         [(None, 224, 224, 3)]     0         
_________________________________________________________________
block1_conv1 (Conv2D)        (None, 224, 224, 64)      1792      
_________________________________________________________________
block1_conv2 (Conv2D)        (None, 224, 224, 64)      36928     
_________________________________________________________________
block1_pool (MaxPooling2D)   (None, 112, 112, 64)      0         
_________________________________________________________________
block2_conv1 (Conv2D)        (None, 112, 112, 128)     73856     
_________________________________________________________________
block2_conv2 (Conv2D)        (None, 112, 112, 128)     147584    
_________________________________________________________________
block2_pool (MaxPooling2D)   (None, 56, 56, 128)       0         
_________________________________________________________________
block3_conv1 (Conv2D)        (None, 56, 56, 256)       295168    
_________________________________________________________________
block3_conv2 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv3 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_conv4 (Conv2D)        (None, 56, 56, 256)       590080    
_________________________________________________________________
block3_pool (MaxPooling2D)   (None, 28, 28, 256)       0         
_________________________________________________________________
block4_conv1 (Conv2D)        (None, 28, 28, 512)       1180160   
_________________________________________________________________
block4_conv2 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv3 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_conv4 (Conv2D)        (None, 28, 28, 512)       2359808   
_________________________________________________________________
block4_pool (MaxPooling2D)   (None, 14, 14, 512)       0         
_________________________________________________________________
block5_conv1 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv2 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv3 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_conv4 (Conv2D)        (None, 14, 14, 512)       2359808   
_________________________________________________________________
block5_pool (MaxPooling2D)   (None, 7, 7, 512)         0         
_________________________________________________________________
global_average_pooling2d (Gl (None, 512)               0         
_________________________________________________________________
dense (Dense)                (None, 1024)              525312    
_________________________________________________________________
dense_1 (Dense)              (None, 2)                 2050      
=================================================================
Total params: 20,551,746
Trainable params: 0
Non-trainable params: 20,551,746
_________________________________________________________________

試したこと

入力するデータ量を減らしてみたりしました.

補足情報（FW/ツールのバージョンなど）

Google Colaboratory上で実行しています.

Comments

[hoshi-takanori]

GPU 使ってます？
https://qiita.com/tomo_makes/items/f70fe48c428d3a61e131
https://www.tdi.co.jp/miso/google-colaboratory-gpu

[fiveHundred]

GPUやTPUは使っておりますか？

[mahc]

import tensorflow as tf
tf.test.gpu_device_name()
と打つと
''
と出るのでおそらくGPU動いてないですね...
申し訳ないです,初歩的なミスでした...

[mahc]

GPU使うと動きました...
ありがとうございました！

Answer 1

GPUを使ってなかったです.
すいませんでした.
動きました...