When I run the following code:
init = "he_uniform"
inp = Input(shape=(1800, 1))
norm1 = BatchNormalization(mode=0)(inp)
conv1 = Convolution1D(16, 5, border_mode='same', init=init, activation="relu")(norm1)
pool1 = MaxPooling1D(pool_size=3)(conv1)
norm2 = BatchNormalization(mode=0)(pool1)
flat1 = Flatten()(norm2)
dens1 = Dense(128, init=init, activation="relu")(flat1)
#norm3 = BatchNormalization(mode=0)(dens1)
output = Dense(2, init=init, activation="softmax")(dens1)
from keras.models import *
model = Model(input=[inp], output=output)
I had the warnings:
root/miniconda/envs/jupyterhub_py3/lib/python3.4/site-packages/ipykernel/__main__.py:4: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization()`
/root/miniconda/envs/jupyterhub_py3/lib/python3.4/site-packages/ipykernel/__main__.py:5: UserWarning: Update your `Conv1D` call to the Keras 2 API: `Conv1D(16, 5, activation="relu", kernel_initializer="he_uniform", padding="same")`
/root/miniconda/envs/jupyterhub_py3/lib/python3.4/site-packages/ipykernel/__main__.py:7: UserWarning: Update your `BatchNormalization` call to the Keras 2 API: `BatchNormalization()`
/root/miniconda/envs/jupyterhub_py3/lib/python3.4/site-packages/ipykernel/__main__.py:9: UserWarning: Update your `Dense` call to the Keras 2 API: `Dense(128, activation="relu", kernel_initializer="he_uniform")`
The following approach did not help.
with warnings.catch_warnings():
warnings.simplefilter("ignore")
How to disable this warning?
These warnings are about how Keras uses TensorFlow. The people who maintain Keras are the ones who can fix them. The warnings in TensorFlow could be managed by the tf.logging module. To turn off the warnings you can use,
From within python: The warnings indicate you are using keras 1.x code with a keras 2.x installation. Maybe it would be easier to fix them, instead of ignoring. Show activity on this post.
a deprecation notice means it works but will soon be removed. so if you are maintaining this code, you should replace it. but for a one off investigation, it's fine. These warnings are about how Keras uses TensorFlow. The people who maintain Keras are the ones who can fix them.
Using a start/stop/resume training approach with Keras, we have achieved 94.14% validation accuracy. At this point the learning rate has become so small that the corresponding weight updates are also very small, implying that the model cannot learn much more. I only allowed training to continue for 5 epochs before killing the script.
You can tell tensorflow using environment variables. From within python:
import os
os.environ['TF_CPP_MIN_LOG_LEVEL'] = '3'
The warnings indicate you are using keras 1.x code with a keras 2.x installation. Maybe it would be easier to fix them, instead of ignoring.
You can use this code at the top of the main.py:
def warn(*args, **kwargs):
pass
import warnings
warnings.warn = warn
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