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FIX possible detrimental tf api usage #9

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FIX possible detrimental tf api usage #9

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34 changes: 23 additions & 11 deletions examples/lib/mnist.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,8 +3,11 @@


def _preprocess_image(image):
return tf.to_float(image) / 255 - 0.5

if(int(tf.__version__.split(".")[1])<13 and int(tf.__version__.split(".")[0])<2): ### For tf version < 1.13.0
return tf.to_float(image) / 255 - 0.5
else: ### For tf version >= 1.13.0
return tf.cast(image,tf.float32) / 255 - 0.5


def read_file(filename_queue):
_, serialized = tf.TFRecordReader().read(filename_queue)
Expand All @@ -31,9 +34,14 @@ def serving_input_fn():


def minimize(loss):
return tf.train.AdamOptimizer().minimize(
loss,
tf.contrib.framework.get_global_step())
if(int(tf.__version__.split(".")[1])<4 and int(tf.__version__.split(".")[0])==1) or int(tf.__version__.split(".")[0]) == 0: ### for tf version <1.4.0
return tf.train.AdamOptimizer().minimize(
loss,
tf.contrib.framework.get_global_step())
else: ###for version >= 1.4.0
return tf.train.AdamOptimizer().minimize(
loss,
tf.train.get_global_step())


def def_model():
Expand All @@ -53,10 +61,14 @@ def model(image, number=None, mode=None):
loss = tf.reduce_mean(
tf.nn.sparse_softmax_cross_entropy_with_logits(labels=number,
logits=h))

return predictions, loss, minimize(loss), {
"accuracy": tf.contrib.metrics.streaming_accuracy(predictions,
number)[1],
}

if(int(tf.__version__.split(".")[1])<5 and int(tf.__version__.split(".")[0])==1) or int(tf.__version__.split(".")[0]) == 0: ### for tf version <1.5.0
return predictions, loss, minimize(loss), {
"accuracy": tf.contrib.metrics.streaming_accuracy(predictions,
number)[1],
}
else: ###for version >= 1.5.0 the new api changed signature sequence
return predictions, loss, minimize(loss), {
"accuracy": tf.metrics.accuracy(number,
predictions)[1],
}
return model