Updating and refactored project to TensorFlow 2.1.0

.gitignore

@@ -0,0 +1,6 @@
+__pycache__
+.DS_Store
+*.zip
+env
+model
+.ipynb_checkpoints

requirements.txt

@@ -1,14 +1,14 @@
-absl-py==0.4.1
+absl-py==0.7.0
 astor==0.7.1
 backcall==0.1.0
 bleach==2.1.4
 cloudpickle==0.5.5
 cycler==0.10.0
-dask==0.19.0
+dask==1.0.0
 decorator==4.3.0
 entrypoints==0.2.3
-gast==0.2.0
-grpcio==1.14.2
+gast==0.2.2
+grpcio==1.24.3
 h5py==2.8.0
 html5lib==1.0.1
 imageio==2.4.0
@@ -32,16 +32,15 @@ nbconvert==5.3.1
 nbformat==4.4.0
 networkx==2.1
 notebook==5.7.8
-numpy==1.14.5
+numpy==1.16.0
 pandocfilters==1.4.2
 parso==0.3.1
 pexpect==4.6.0
 pickleshare==0.7.4
 Pillow==5.2.0
-pkg-resources==0.0.0
 prometheus-client==0.3.1
 prompt-toolkit==1.0.15
-protobuf==3.6.1
+protobuf==3.8.0
 ptyprocess==0.6.0
 Pygments==2.2.0
 pyparsing==2.2.0
@@ -50,13 +49,13 @@ pytz==2018.5
 PyWavelets==1.0.0
 pyzmq==17.1.2
 qtconsole==4.4.1
-scikit-image==0.14.0
-scipy==1.1.0
+scikit-image==0.14.2
+scipy==1.4.1
 Send2Trash==1.5.0
 simplegeneric==0.8.1
-six==1.11.0
-tensorboard==1.10.0
-tensorflow==1.10.1
+six==1.12.0
+tensorboard==2.1.0
+tensorflow==2.1.0
 termcolor==1.1.0
 terminado==0.8.1
 testpath==0.3.1

src/demo.ipynb

@@ -57,7 +57,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "sess = tf.InteractiveSession()\n",
+    "sess = tf.compat.v1.InteractiveSession()\n",
     "cnn = LicensePlatesCNN(sess, checkpoint_dir, summary_dir)\n",
     "cnn.load();"
    ]

src/eval.py

@@ -9,7 +9,7 @@
 
 
 def eval(test_data, store_results_path, input_channels=3, checkpoint_dir="checkpoint", summary_dir="summary"):
-    with tf.Session() as sess:
+    with tf.compat.v1.Session() as sess:
         cnn = LicensePlatesCNN(sess=sess,
                                checkpoint_dir=checkpoint_dir,
                                summary_dir=summary_dir,

src/ops.py

@@ -5,14 +5,14 @@ def conv2d(x, W):
     """
     Returns a 2D convolutional layer with full stride.
     """
-    return tf.nn.conv2d(x, W, strides=[1, 1, 1, 1], padding="SAME")
+    return tf.nn.conv2d(input=x, filters=W, strides=[1, 1, 1, 1], padding="SAME")
 
 
 def max_pool_2x2(x):
     """
     Max-pooling over 2x2 blocks
     """
-    return tf.nn.max_pool(x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
+    return tf.nn.max_pool2d(input=x, ksize=[1, 2, 2, 1], strides=[1, 2, 2, 1], padding="SAME")
 
 
 def weights_variable_truncated_normal(shape, mean=0.0, stddev=0.1, name=None, trainable=True):
@@ -28,7 +28,7 @@ def weights_variable_truncated_normal(shape, mean=0.0, stddev=0.1, name=None, tr
         the default list of variables to use by the `Optimizer` classes.
     :return: Weight matrix
     """
-    initial = tf.truncated_normal(shape, mean=mean, stddev=stddev)
+    initial = tf.random.truncated_normal(shape, mean=mean, stddev=stddev)
     return tf.Variable(initial, trainable=trainable, name=name)
 
 
@@ -43,7 +43,7 @@ def weights_variable_xavier(shape, name, trainable=True):
         the default list of variables to use by the `Optimizer` classes.
     :return: Weight matrix
     """
-    return tf.get_variable(name, shape=shape, initializer=tf.contrib.layers.xavier_initializer(), trainable=trainable)
+    return tf.compat.v1.get_variable(name, shape=shape, initializer=tf.compat.v1.keras.initializers.VarianceScaling(scale=1.0, mode="fan_avg", distribution="uniform"), trainable=trainable)
 
 
 def bias_variable(shape, value=0, name=None, trainable=True):

src/train.py

@@ -12,7 +12,7 @@
 parser.add_argument("--summary_dir", type=str, help="Directory where to store summary", default="summary")
 args = vars(parser.parse_args())
 
-with tf.Session() as sess:
+with tf.compat.v1.Session() as sess:
     cnn = LicensePlatesCNN(sess,
                            args["checkpoint_dir"],
                            args["summary_dir"],