machine-learning - Colab TPU エラー - InvalidArgumentError: TPU でサポートされていないデータ型: double、出力 cond_8/Merge:0 が原因

Question

TPUを使用してGoogle Colabで基本的な文字分類を実行しようとしています。次のエラーが表示されます。

InvalidArgumentError: Unsupported data type for TPU: double, caused by output cond_8/Merge:0

numpy 配列を作成するときに float32 を使用しているため、問題が何であるかはわかりません。cond_8/Merge:0 が何を指しているのかもわかりません。ロードする入力ファイルは、多くの 28x28 グレースケール画像を表す JSON 配列です

[{"label":25,"data":[[[1],[.56720000]...],...]}]

最初の入力レイヤーを除くすべてのレイヤーをコメントアウトしようとしましたが、それでも問題が発生します!! 私のコードは次のとおりです。

import os, re, math, json, shutil, pprint
import PIL.Image, PIL.ImageFont, PIL.ImageDraw
import numpy as np
import json
import tensorflow as tf
from matplotlib import pyplot as plt
from tensorflow.python.platform import tf_logging
from google.colab import drive
print("Tensorflow version " + tf.__version__)

with open('/tmp/encoded.json') as json_file:
    data = json.load(json_file)

print("Got data")

images_data = list(map(lambda row: row["data"],data))
label_data = list(map(lambda row: row["label"],data))

print("mapped data")

images_data_tensor = np.asarray(images_data, dtype=np.float32)
label_data_tensor = np.asarray(label_data, dtype=np.float32)

print("converted to tensors")

BATCH_SIZE = 128


N = 24


# This model trains to 99.4% sometimes 99.5% accuracy in 10 epochs (with a batch size of 32)
def create_model():
  l = tf.keras.layers
  model = tf.keras.Sequential(
    [
      #l.Reshape(input_shape=(28*28,), target_shape=(28, 28, 1)),

      l.Conv2D(input_shape=(28,28,1,), filters=6, kernel_size=3, padding='same', use_bias=False), # no bias necessary before batch norm
      l.BatchNormalization(scale=False, center=True), # no batch norm scaling necessary before "relu"
      l.Activation('relu'), # activation after batch norm

      l.Conv2D(filters=12, kernel_size=6, padding='same', use_bias=False, strides=2),
      l.BatchNormalization(scale=False, center=True),
      l.Activation('relu'),

      l.Conv2D(filters=24, kernel_size=6, padding='same', use_bias=False, strides=2),
      l.BatchNormalization(scale=False, center=True),
      l.Activation('relu'),

      l.Flatten(),
      l.Dense(200, use_bias=False),
      l.BatchNormalization(scale=False, center=True),
      l.Activation('relu'),
      l.Dropout(0.5), # Dropout on dense layer only

      l.Dense(10, activation='softmax')
    ])
  return model

# set up learning rate decay
lr_decay = tf.keras.callbacks.LearningRateScheduler(lambda epoch: 0.0001 + 0.02 * math.pow(0.5, 1+epoch), verbose=True)


EPOCHS = 10
tpu = None

# Default strategy for GPU/CPU. Note that tensorflow-gpu will need to be installed for GPU to work
strategy = tf.distribute.MirroredStrategy()

try: # TPU detection
  tpu = tf.distribute.cluster_resolver.TPUClusterResolver() # Picks up a connected TPU on Google's Colab, ML Engine, Kubernetes and Deep Learning VMs accessed through the 'ctpu up' utility
  #tpu = tf.distribute.cluster_resolver.TPUClusterResolver('MY_TPU_NAME') # If auto-detection does not work, you can pass the name of the TPU explicitly (tip: on a VM created with "ctpu up" the TPU has the same name as the VM)
  tf.tpu.experimental.initialize_tpu_system(tpu)
  strategy = tf.distribute.experimental.TPUStrategy(tpu)
except ValueError:
  print('Training on CPU')

with strategy.scope():
  trained_model = create_model()
  trained_model.compile(optimizer='adam', # learning rate will be set by LearningRateScheduler
                loss='categorical_crossentropy',
                metrics=['accuracy'])

  # print model layers
  trained_model.summary()

  history = trained_model.fit(x=images_data_tensor,y=label_data_tensor, epochs=EPOCHS, callbacks=[lr_decay])  


print(history.history.keys())