Update the to_categorical in the data_utils.py

ACKNOWLEDGMENTS

@@ -2,7 +2,8 @@ TFLearn was created to provide a new transparent and simple interface over Tenso
 
 - [TensorFlow](http://tensorflow.org) for some derivated code (copyright below). As a higher-level API, TFLearn is heavily relying on TensorFlow base API.
 - [TensorFlow models](https://github.com/tensorflow/models).
-- [Lasagne](https://github.com/Lasagne/Lasagne) (MIT License) TFLearn model building concept with layers is directly inspired from Lasagne. While Lasagne only support Theano, TFLearn is aiming at providing similar concept over TensorFlow instead.
+- [SKLearn](http://scikit-learn.org) (NEW BSD License) TFLearn estimators are an adaptation of SKLearn in TensorFLow, as such, TFLearn reuses some structure and documentation from it.
+- [Lasagne](https://github.com/Lasagne/Lasagne) (MIT License) Originally, TFLearn neural network building concept is directly inspired from Lasagne. While Lasagne only support Theano, TFLearn is aiming at providing similar concept over TensorFlow instead.
 - [Keras](http://keras.io) (MIT License) A few layers structure.
 
 # TensorFlow is subject to the following copyright notice:
@@ -210,3 +211,37 @@ Copyright 2015 The TensorFlow Authors.  All rights reserved.
    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.
+
+# SKLearn is subject to the following license:
+
+New BSD License
+
+Copyright (c) 2007–2017 The scikit-learn developers.
+All rights reserved.
+
+
+Redistribution and use in source and binary forms, with or without
+modification, are permitted provided that the following conditions are met:
+
+  a. Redistributions of source code must retain the above copyright notice,
+     this list of conditions and the following disclaimer.
+  b. Redistributions in binary form must reproduce the above copyright
+     notice, this list of conditions and the following disclaimer in the
+     documentation and/or other materials provided with the distribution.
+  c. Neither the name of the Scikit-learn Developers  nor the names of
+     its contributors may be used to endorse or promote products
+     derived from this software without specific prior written
+     permission.
+
+
+THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS"
+AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE
+IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE
+ARE DISCLAIMED. IN NO EVENT SHALL THE REGENTS OR CONTRIBUTORS BE LIABLE FOR
+ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL
+DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR
+SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER
+CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT
+LIABILITY, OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY
+OUT OF THE USE OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH
+DAMAGE.

README.md

@@ -18,7 +18,7 @@ TFLearn features include:
 
 The high-level API currently supports most of recent deep learning models, such as Convolutions, LSTM, BiRNN, BatchNorm, PReLU, Residual networks, Generative networks... In the future, TFLearn is also intended to stay up-to-date with latest deep learning techniques.
 
-Note: Latest TFLearn (v0.3) is only compatible with TensorFlow v1.0 and over.
+Note: Latest TFLearn (v0.5) is only compatible with TensorFlow v2.0 and over.
 
 ## Overview
 ```python
@@ -50,11 +50,18 @@ model.generate(50, temperature=1.0)
 
 There are many more examples available *[here](http://tflearn.org/examples)*.
 
+## Compatibility
+TFLearn is based on the original tensorflow v1 graph API. When using TFLearn, make sure to import tensorflow that way:
+```
+import tflearn
+import tensorflow.compat.v1 as tf
+```
+
 ## Installation
 
 **TensorFlow Installation**
 
-TFLearn requires Tensorflow (version 1.0+) to be installed.
+TFLearn requires Tensorflow (version 2.0+) to be installed.
 
 To install TensorFlow, simply run:
 ```
@@ -65,7 +72,7 @@ or, with GPU-support:
 pip install tensorflow-gpu
 ```
 
-For more details see *[TensorFlow installation instructions](https://github.com/tensorflow/tensorflow/blob/master/tensorflow/g3doc/get_started/os_setup.md)*
+For more details see *[TensorFlow installation instructions](https://www.tensorflow.org/install)*
 
 **TFLearn Installation**
 

RELEASE.md

@@ -1,3 +1,22 @@
+# Release 0.5.0
+
+Major changes:
+- TensorFlow 2.3.0 support
+- Refactoring source to use tf.compat.v1
+
+Minor changes:
+- Update documentation
+- Various bug fix
+
+# Release 0.4.0
+
+Major changes:
+- Added new estimators (RandomForest, KMeans)
+
+Minor changes:
+- Added distance ops
+- Various bug fix
+
 # Release 0.3.2
 
 Major changes:

docs/autodoc.py

@@ -17,7 +17,7 @@
 from tflearn import objectives
 from tflearn import optimizers
 from tflearn import data_utils
-from tflearn import losses
+from tflearn import regularizers
 from tflearn import summaries
 from tflearn import utils
 from tflearn import variables
@@ -48,7 +48,7 @@
            (objectives, 'tflearn.objectives'),
            (optimizers, 'tflearn.optimizers'),
            (data_utils, 'tflearn.data_utils'),
-           (losses, 'tflearn.losses'),
+           (regularizers, 'tflearn.regularizers'),
            (summaries, 'tflearn.summaries'),
            (variables, 'tflearn.variables'),
            (utils, 'tflearn.utils'),

docs/templates/examples.md

@@ -29,8 +29,10 @@
 - [Highway Convolutional Network](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_highway_mnist.py). Highway Convolutional Network implementation for classifying MNIST dataset.
 - [Residual Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py). A bottleneck residual network applied to MNIST classification task.
 - [Residual Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py). A residual network applied to CIFAR-10 classification task.
-- [ResNeXt (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/resnext_cifar10.py). Aggregated residual transformations network (ResNeXt) applied to CIFAR-10 classification task.
+- [ResNeXt](https://github.com/tflearn/tflearn/blob/master/examples/images/resnext_cifar10.py). Aggregated residual transformations network (ResNeXt) applied to CIFAR-10 classification task.
+- [DenseNet](https://github.com/tflearn/tflearn/blob/master/examples/images/densenet.py). A densely connected convolutional network applied to CIFAR-10 classification task.
 - [Google Inception (v3)](https://github.com/tflearn/tflearn/blob/master/examples/images/googlenet.py). Google's Inception v3 network applied to Oxford Flowers 17 classification task.
+
 ### Unsupervised
 - [Auto Encoder](https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py). An auto encoder applied to MNIST handwritten digits.
 - [Variational Auto Encoder](https://github.com/tflearn/tflearn/blob/master/examples/images/variational_autoencoder.py). A Variational Auto Encoder (VAE) trained to generate digit images.

docs/templates/getting_started.md

@@ -45,7 +45,7 @@ File | Layers
 
 ### Built-in Operations
 
-Besides layers concept, TFLearn also provides many different ops to be used when building a neural network. These ops are firstly mean to be part of the above 'layers' arguments, but they can also be used independently in any other Tensorflow graph for convenience. In practice, just providing the op name as argument is enough (such as activation='relu' or regularizer='L2' for conv_2d), but a function can also be provided for further customization.
+Besides layers concept, TFLearn also provides many different ops to be used when building a neural network. These ops are firstly meant to be part of the above 'layers' arguments, but they can also be used independently in any other Tensorflow graph for convenience. In practice, just providing the op name as argument is enough (such as activation='relu' or regularizer='L2' for conv_2d), but a function can also be provided for further customization.
 
 File | Ops
 -----|----

examples/README.md

@@ -24,12 +24,14 @@
 - [Alexnet](https://github.com/tflearn/tflearn/blob/master/examples/images/alexnet.py). Apply Alexnet to Oxford Flowers 17 classification task.
 - [VGGNet](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network.py). Apply VGG Network to Oxford Flowers 17 classification task.
 - [VGGNet Finetuning (Fast Training)](https://github.com/tflearn/tflearn/blob/master/examples/images/vgg_network_finetuning.py). Use a pre-trained VGG Network and retrain it on your own data, for fast training.
+- [VGG19](https://github.com/AhmetHamzaEmra/tflearn/blob/master/examples/images/VGG19.py). Apply VGG19 Network to ImageNet classification task.
 - [RNN Pixels](https://github.com/tflearn/tflearn/blob/master/examples/images/rnn_pixels.py). Use RNN (over sequence of pixels) to classify images.
 - [Highway Network](https://github.com/tflearn/tflearn/blob/master/examples/images/highway_dnn.py). Highway Network implementation for classifying MNIST dataset.
 - [Highway Convolutional Network](https://github.com/tflearn/tflearn/blob/master/examples/images/convnet_highway_mnist.py). Highway Convolutional Network implementation for classifying MNIST dataset.
 - [Residual Network (MNIST)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_mnist.py). A bottleneck residual network applied to MNIST classification task.
 - [Residual Network (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/residual_network_cifar10.py). A residual network applied to CIFAR-10 classification task.
-- [ResNeXt (CIFAR-10)](https://github.com/tflearn/tflearn/blob/master/examples/images/resnext_cifar10.py). Aggregated residual transformations network (ResNeXt) applied to CIFAR-10 classification task.
+- [ResNeXt](https://github.com/tflearn/tflearn/blob/master/examples/images/resnext_cifar10.py). Aggregated residual transformations network (ResNeXt) applied to CIFAR-10 classification task.
+- [DenseNet](https://github.com/tflearn/tflearn/blob/master/examples/images/densenet.py). A densely connected convolutional network applied to CIFAR-10 classification task.
 - [Google Inception (v3)](https://github.com/tflearn/tflearn/blob/master/examples/images/googlenet.py). Google's Inception v3 network applied to Oxford Flowers 17 classification task.
 ### Unsupervised
 - [Auto Encoder](https://github.com/tflearn/tflearn/blob/master/examples/images/autoencoder.py). An auto encoder applied to MNIST handwritten digits.

examples/basics/kmeans.py

@@ -0,0 +1,26 @@
+""" K-Means Example """
+
+from __future__ import division, print_function, absolute_import
+
+from tflearn.estimators import KMeans
+
+# Data loading and preprocessing
+import tflearn.datasets.mnist as mnist
+X, Y, testX, testY = mnist.load_data(one_hot=False)
+
+# K-Means training
+m = KMeans(n_clusters=10, distance='squared_euclidean')
+m.fit(X, display_step=10)
+
+# Testing
+print("Clusters center coordinates:")
+print(m.cluster_centers_vars)
+
+print("X[0] nearest cluster:")
+print(m.labels_[0])
+
+print("Predicting testX[0] nearest cluster:")
+print(m.predict(testX[0]))
+
+print("Transforming testX[0] to a cluster-distance space:")
+print(m.transform(testX[0]))

examples/basics/logical.py

@@ -5,7 +5,7 @@
 
 from __future__ import absolute_import, division, print_function
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 import tflearn
 
 # Logical NOT operator

examples/basics/random_forest.py

@@ -0,0 +1,24 @@
+""" Random Forest example. """
+
+from __future__ import division, print_function, absolute_import
+
+import tflearn
+from tflearn.estimators import RandomForestClassifier
+
+# Data loading and pre-processing
+import tflearn.datasets.mnist as mnist
+X, Y, testX, testY = mnist.load_data(one_hot=False)
+
+m = RandomForestClassifier(n_estimators=100, max_nodes=1000)
+m.fit(X, Y, batch_size=10000, display_step=10)
+
+print("Compute the accuracy on train set:")
+print(m.evaluate(X, Y, tflearn.accuracy_op))
+
+print("Compute the accuracy on test set:")
+print(m.evaluate(testX, testY, tflearn.accuracy_op))
+
+print("Digits for test images id 0 to 5:")
+print(m.predict(testX[:5]))
+print("True digits:")
+print(testY[:5])

examples/basics/use_dask.py

@@ -19,8 +19,8 @@
 # Load CIFAR-10 Dataset
 from tflearn.datasets import cifar10
 (X, Y), (X_test, Y_test) = cifar10.load_data()
-Y = to_categorical(Y, 10)
-Y_test = to_categorical(Y_test, 10)
+Y = to_categorical(Y)
+Y_test = to_categorical(Y_test)
 
 # Create DASK array using numpy arrays
 # (Note that it can work with HDF5 Dataset too)

examples/basics/use_hdf5.py

@@ -19,8 +19,8 @@
 # CIFAR-10 Dataset
 from tflearn.datasets import cifar10
 (X, Y), (X_test, Y_test) = cifar10.load_data()
-Y = to_categorical(Y, 10)
-Y_test = to_categorical(Y_test, 10)
+Y = to_categorical(Y)
+Y_test = to_categorical(Y_test)
 
 # Create a hdf5 dataset from CIFAR-10 numpy array
 import h5py

examples/basics/weights_loading_scope.py

@@ -10,7 +10,7 @@
 
 import re
 import tflearn
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 import tflearn.datasets.mnist as mnist
 
 from tflearn.layers.core import input_data, dropout, fully_connected

examples/extending_tensorflow/builtin_ops.py

@@ -5,7 +5,7 @@
 Tensorflow graph.
 """
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 import tflearn
 
 # ----------------------------------

examples/extending_tensorflow/layers.py

@@ -4,7 +4,7 @@
 """
 from __future__ import print_function
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 import tflearn
 
 # --------------------------------------
@@ -39,25 +39,25 @@
     net = tflearn.fully_connected(net, 10, activation='linear')
 
     # Defining other ops using Tensorflow
-    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(net, Y))
+    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits_v2(logits=net, labels=Y))
     optimizer = tf.train.AdamOptimizer(learning_rate=0.01).minimize(loss)
 
     # Initializing the variables
-    init = tf.initialize_all_variables()
+    init = tf.global_variables_initializer()
 
     # Launch the graph
     with tf.Session() as sess:
         sess.run(init)
 
         batch_size = 128
-        for epoch in range(2): # 2 epochs
+        for epoch in range(2):  # 2 epochs
             avg_cost = 0.
-            total_batch = int(mnist_data.train.num_examples/batch_size)
+            total_batch = int(mnist_data.train.num_examples / batch_size)
             for i in range(total_batch):
                 batch_xs, batch_ys = mnist_data.train.next_batch(batch_size)
                 sess.run(optimizer, feed_dict={X: batch_xs, Y: batch_ys})
                 cost = sess.run(loss, feed_dict={X: batch_xs, Y: batch_ys})
-                avg_cost += cost/total_batch
+                avg_cost += cost / total_batch
                 if i % 20 == 0:
-                    print("Epoch:", '%03d' % (epoch+1), "Step:", '%03d' % i,
+                    print("Epoch:", '%03d' % (epoch + 1), "Step:", '%03d' % i,
                           "Loss:", str(cost))

examples/extending_tensorflow/summaries.py

@@ -32,7 +32,7 @@
 
 """
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 import tflearn
 
 # Loading MNIST dataset
@@ -74,11 +74,13 @@ def dnn(x):
         return x
 
     net = dnn(X)
-    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(net, Y))
-    optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
-    accuracy = tf.reduce_mean(
-        tf.cast(tf.equal(tf.argmax(net, 1), tf.argmax(Y, 1)), tf.float32),
-        name="acc")
+
+    with tf.name_scope('Summaries'):
+        loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(logits=net,labels=Y))
+        optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
+        accuracy = tf.reduce_mean(
+            tf.cast(tf.equal(tf.argmax(net, 1), tf.argmax(Y, 1)), tf.float32),
+            name="acc")
 
     # construct two varaibles to add as additional "valiation monitors"
     # these varaibles are evaluated each time validation happens (eg at a snapshot)
@@ -92,11 +94,10 @@ def dnn(x):
     with tf.name_scope('CustomMonitor'):
         test_var = tf.reduce_sum(tf.cast(net, tf.float32), name="test_var")
         test_const = tf.constant(32.0, name="custom_constant")
-
-    # Define a train op
+        # Define a train op
     trainop = tflearn.TrainOp(loss=loss, optimizer=optimizer,
-                              validation_monitors=[test_var, test_const],
-                              metric=accuracy, batch_size=128)
+                            validation_monitors=[test_var, test_const],
+                            metric=accuracy, batch_size=128)
 
     # Tensorboard logs stored in /tmp/tflearn_logs/. Using verbose level 2.
     trainer = tflearn.Trainer(train_ops=trainop,

examples/extending_tensorflow/trainer.py

@@ -3,7 +3,7 @@
 TFLearn wrappers regular Tensorflow expressions.
 """
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 import tflearn
 
 # ----------------------------

examples/extending_tensorflow/variables.py

@@ -7,7 +7,7 @@
 variables.
 """
 
-import tensorflow as tf
+import tensorflow.compat.v1 as tf
 import tflearn
 import tflearn.variables as va
 
@@ -48,7 +48,8 @@ def dnn(x):
         return x
 
     net = dnn(X)
-    loss = tf.reduce_mean(tf.nn.softmax_cross_entropy_with_logits(net, Y))
+    loss = tf.reduce_mean(
+        tf.nn.softmax_cross_entropy_with_logits(logits=net, labels=Y))
     optimizer = tf.train.GradientDescentOptimizer(learning_rate=0.1)
     accuracy = tf.reduce_mean(
         tf.cast(tf.equal(tf.argmax(net, 1), tf.argmax(Y, 1)), tf.float32),