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I use tf.contrib.tpu.keras_to_tpu_model to make my code be able to run on TPU,but it took 170 hours to finish an epoch while CPU took the same time and GPU took only 40 hours per epoch.I tried to adjust batch size but nothing changed.And I've tested the input function may take up 20% of the run time when running on GPU, so I think it's maybe not the main reason.

Here is my code:https://github.com/WangHexie/DHNE/blob/master/src/hypergraph_embedding.py

Run on colab:

1. TPU:https://colab.research.google.com/gist/WangHexie/30c385509f9cd93be747f04c39f039a4/tpu-error.ipynb


2. GPU：https://colab.research.google.com/gist/WangHexie/5bfac53bf92ef0ad527f15ddbf8705e1/-gpu-ipynb.ipynb
   

The model:

def build_model(self):
 self.inputs = [Input(shape=(self.options.dim_feature[i], ), name='input_'.format(i), dtype='float') for i in range(3)]

 self.encodeds = [Dense(self.options.embedding_size[i], activation='tanh', name='encode_'.format(i))(self.inputs[i]) for i in range(3)]
 self.decodeds = [Dense(self.options.dim_feature[i], activation='sigmoid', name='decode_'.format(i),
 activity_regularizer = regularizers.l2(0.0))(self.encodeds[i]) for i in range(3)]

 self.merged = concatenate(self.encodeds, axis=1)
 self.hidden_layer = Dense(self.options.hidden_size, activation='tanh', name='full_connected_layer')(self.merged)
 self.ouput_layer = Dense(1, activation='sigmoid', name='classify_layer')(self.hidden_layer)

 self.model = Model(inputs=self.inputs, outputs=self.decodeds+[self.ouput_layer])

 self.model.compile(optimizer=tf.train.AdamOptimizer(learning_rate=self.options.learning_rate),
 loss=[self.sparse_autoencoder_error]*3+['binary_crossentropy'],
 loss_weights=[self.options.alpha]*3+[1.0],
 metrics=dict([('decode_'.format(i), 'mse') for i in range(3)]+[('classify_layer', 'accuracy')]))
 self.model = tf.contrib.tpu.keras_to_tpu_model(
 self.model,
 strategy=tf.contrib.tpu.TPUDistributionStrategy(
 tf.contrib.cluster_resolver.TPUClusterResolver(
 tpu='grpc://' + os.environ['COLAB_TPU_ADDR'])
 )
 )
 self.model.summary()