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#!/usr/bin/python
#
# Copyright 2018 Google LLC
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
#      http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.

from __future__ import absolute_import

import tensorflow as tf
import numpy as np


tf.keras.backend.clear_session()

# Load data
(train_images, train_labels), (test_images, test_labels) = tf.keras.datasets.mnist.load_data()


TRAINING_SIZE = len(train_images)
TEST_SIZE = len(test_images)

# Reshape from (N, 28, 28) to (N, 784)
train_images = np.reshape(train_images, (TRAINING_SIZE, 784))
test_images = np.reshape(test_images, (TEST_SIZE, 784))

# Convert the array to float32 as opposed to uint8
train_images = train_images.astype(np.float32)
test_images = test_images.astype(np.float32)

# Convert the pixel values from integers between 0 and 255 to floats between 0 and 1
train_images /= 255
test_images /=  255



NUM_DIGITS = 10

print("Before", train_labels[0]) # The format of the labels before conversion

train_labels  = tf.keras.utils.to_categorical(train_labels, NUM_DIGITS)

print("After", train_labels[0]) # The format of the labels after conversion

test_labels = tf.keras.utils.to_categorical(test_labels, NUM_DIGITS)



# Cast the labels to floats, needed later
train_labels = train_labels.astype(np.float32)
test_labels = test_labels.astype(np.float32)


tf.keras.backend.clear_session()

model = tf.keras.Sequential()
model.add(tf.keras.layers.Dense(512, activation='relu', input_shape=(784,), name='dense_1'))
model.add(tf.keras.layers.Dense(10, activation=tf.nn.softmax, name='dense_2'))
# Notes: Make sure each layer above has explicit names to support tfjs.

# Create a TensorFlow optimizer, rather than using the Keras version
# This is currently necessary when working in eager mode
optimizer = tf.train.RMSPropOptimizer(learning_rate=0.001)

# We will now compile and print out a summary of our model
model.compile(loss='categorical_crossentropy',
              optimizer=optimizer,
              metrics=['accuracy'])

model.summary()


BATCH_SIZE=128
EPOCHS=5

model.fit(train_images, train_labels, epochs=EPOCHS, batch_size=BATCH_SIZE)

print("Starting evaluation")
loss, accuracy = model.evaluate(test_images, test_labels)
print('Test accuracy: %.2f' % (accuracy))

model.save('model.h5')


    

    




---------------------------------------------------------------------------
ImportError                               Traceback (most recent call last)
/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow.py in <module>()
     57 
---> 58   from tensorflow.python.pywrap_tensorflow_internal import *
     59   from tensorflow.python.pywrap_tensorflow_internal import __version__

/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py in <module>()
     27             return _mod
---> 28     _pywrap_tensorflow_internal = swig_import_helper()
     29     del swig_import_helper

/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py in swig_import_helper()
     23             try:
---> 24                 _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
     25             finally:

/usr/lib/python3.5/imp.py in load_module(name, file, filename, details)
    241         else:
--> 242             return load_dynamic(name, filename, file)
    243     elif type_ == PKG_DIRECTORY:

/usr/lib/python3.5/imp.py in load_dynamic(name, path, file)
    341             name=name, loader=loader, origin=path)
--> 342         return _load(spec)
    343 

ImportError: libcuda.so.1: cannot open shared object file: No such file or directory

During handling of the above exception, another exception occurred:

ImportError                               Traceback (most recent call last)
<ipython-input-1-cc2ca7253c99> in <module>()
     17 from __future__ import absolute_import
     18 
---> 19 import tensorflow as tf
     20 import numpy as np
     21 

/usr/local/lib/python3.5/dist-packages/tensorflow/__init__.py in <module>()
     20 
     21 # pylint: disable=g-bad-import-order
---> 22 from tensorflow.python import pywrap_tensorflow  # pylint: disable=unused-import
     23 
     24 try:

/usr/local/lib/python3.5/dist-packages/tensorflow/python/__init__.py in <module>()
     47 import numpy as np
     48 
---> 49 from tensorflow.python import pywrap_tensorflow
     50 
     51 # Protocol buffers

/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow.py in <module>()
     72 for some common reasons and solutions.  Include the entire stack trace
     73 above this error message when asking for help.""" % traceback.format_exc()
---> 74   raise ImportError(msg)
     75 
     76 # pylint: enable=wildcard-import,g-import-not-at-top,unused-import,line-too-long

ImportError: Traceback (most recent call last):
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow.py", line 58, in <module>
    from tensorflow.python.pywrap_tensorflow_internal import *
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 28, in <module>
    _pywrap_tensorflow_internal = swig_import_helper()
  File "/usr/local/lib/python3.5/dist-packages/tensorflow/python/pywrap_tensorflow_internal.py", line 24, in swig_import_helper
    _mod = imp.load_module('_pywrap_tensorflow_internal', fp, pathname, description)
  File "/usr/lib/python3.5/imp.py", line 242, in load_module
    return load_dynamic(name, filename, file)
  File "/usr/lib/python3.5/imp.py", line 342, in load_dynamic
    return _load(spec)
ImportError: libcuda.so.1: cannot open shared object file: No such file or directory


Failed to load the native TensorFlow runtime.

See https://www.tensorflow.org/install/install_sources#common_installation_problems

for some common reasons and solutions.  Include the entire stack trace
above this error message when asking for help.

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