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README.md

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+# Human-Rights-Archive-CNNs
+Human Rights Violations Recognition with Deep Learning in Keras
+
+## Overview
+This repository contains the code (written in Keras) for the 
+task of learning & transferring image representations for recognising human rights violations using CNNs.

applications/hra_baseline.py

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+# -*- coding: utf-8 -*-
+"""Simple (baseline) deep CNN on the Human Rights Archive image dataset.
+The baseline model is a simple stack of 3 convolution layers with a ReLU activation
+and followed by max-pooling layers. This is very similar to the architectures that
+Yann LeCun advocated in the 1990s for image classification (with the exception of ReLU).
+
+
+◾◾◾◾ Baseline mode ◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾
+◾                                                                       ◾
+◾ baseline_10epochs  =>  0.137037037037                                 ◾
+◾                                                                       ◾
+◾ baseline_20epochs  =>  0.155555555556                                 ◾
+◾                                                                       ◾
+◾ baseline_40epochs  =>  0.144444444444                                 ◾
+◾                                                                       ◾
+◾                                                                       ◾
+◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾◾
+
+
+# References
+- [https://blog.keras.io/building-powerful-image-classification-models-using-very-little-data.html]
+
+"""
+
+import os
+
+import keras.backend as K
+
+from keras.utils.data_utils import get_file
+from keras.engine.topology import get_source_inputs
+from keras.layers import Dropout
+from keras.layers import Flatten
+from keras.layers import Input
+from keras.layers import Conv2D
+from keras.layers.convolutional import MaxPooling2D
+from keras.layers import Dense
+from keras.optimizers import SGD
+from keras.models import Model
+from keras.applications.imagenet_utils import _obtain_input_shape
+
+
+WEIGHTS_PATH_10_EPOCHS = 'https://github.com/GKalliatakis/crispy-enigma/releases/download/0.5/baseline_model_10_epochs_weights_tf_dim_ordering_tf_kernels.h5'
+WEIGHTS_PATH_20_EPOCHS = 'https://github.com/GKalliatakis/crispy-enigma/releases/download/0.5/baseline_model_20_epochs_weights_tf_dim_ordering_tf_kernels.h5'
+WEIGHTS_PATH_40_EPOCHS = 'https://github.com/GKalliatakis/crispy-enigma/releases/download/0.5/baseline_model_40_epochs_weights_tf_dim_ordering_tf_kernels.h5'
+
+
+
+
+
+def baseline_model(include_top=True, weights='HRA',
+                   input_tensor=None, input_shape=None,
+                   classes=9,
+                   epochs=40):
+    """Instantiates a baseline deep CNN architecture.
+
+    Optionally loads weights pre-trained
+    on ImageNet. Note that when using TensorFlow,
+    for best performance you should set
+    `image_data_format='channels_last'` in your Keras config
+    at ~/.keras/keras.json.
+
+    # Arguments
+        include_top: whether to include the 2 fully-connected
+            layers at the top of the network.
+        weights: one of `None` (random initialization),
+              'HRA' (pre-training on Human Rights Archive),
+              or the path to the weights file to be loaded.
+        input_tensor: optional Keras tensor (i.e. output of `layers.Input()`)
+            to use as image input for the model.
+        classes: optional number of classes to classify images
+            into, only to be specified if `include_top` is True, and
+            if no `weights` argument is specified.
+        epochs: Integer. Number of epochs to train the model.
+        augmented_samples: whether to use the augmented samples during training
+
+    # Returns
+        A Keras model instance.
+
+    # Raises
+        ValueError: in case of invalid argument for `weights`,
+            or invalid input shape.
+
+    """
+
+    if not (weights in {'HRA', None} or os.path.exists(weights)):
+        raise ValueError('The `weights_top_layers` argument should be either '
+                         '`None` (random initialization), `HRA` '
+                         '(pre-training on Human Rights Archive), '
+                         'or the path to the weights file to be loaded.')
+
+    if weights == 'HRA' and include_top and classes != 9:
+        raise ValueError('If using `weights` as Human Rights Archive, `classes` should be 9.')
+
+    # Determine proper input shape
+    input_shape = _obtain_input_shape(input_shape,
+                                      default_size=224,
+                                      min_size=48,
+                                      data_format=K.image_data_format(),
+                                      require_flatten=include_top,
+                                      weights=weights)
+
+    if input_tensor is None:
+        img_input = Input(shape=input_shape)
+    else:
+        if not K.is_keras_tensor(input_tensor):
+            img_input = Input(tensor=input_tensor, shape=input_shape)
+        else:
+            img_input = input_tensor
+
+    # Define The Neural Network Model
+
+    # Block 1
+    x = Conv2D(32, (3, 3), activation='relu', padding='same', name='block1_conv1')(img_input)
+    x = MaxPooling2D((2, 2), strides=(2, 2), name='block1_pool')(x)
+
+    # Block 2
+    x = Conv2D(32, (3, 3), activation='relu', padding='same', name='block2_conv1')(x)
+    x = MaxPooling2D((2, 2), strides=(2, 2), name='block2_pool')(x)
+
+    # Block 3
+    x = Conv2D(64, (3, 3), activation='relu', padding='same', name='block3_conv1')(x)
+    x = MaxPooling2D((2, 2), strides=(2, 2), name='block3_pool')(x)
+    # the model so far outputs 3D feature maps (height, width, features)
+
+    # On top of it we stick two fully-connected layers. We end the model with 9 final outputs representing probabilities for HRA classes
+    # and a softmax activation, which is perfect for a multi-class classification.
+    # To go with it we will also use the categorical_crossentropy loss to train our model.
+    if include_top:
+        # Classification block
+        x = Flatten(name='flatten')(x)
+        x = Dense(64, activation='relu', name='fc1')(x)
+        x = Dropout(0.5)(x)
+        x = Dense(classes, activation='softmax', name='predictions')(x)
+
+    # Ensure that the model takes into account
+    # any potential predecessors of `input_tensor`.
+    if input_tensor is not None:
+        inputs = get_source_inputs(input_tensor)
+    else:
+        inputs = img_input
+    # Create model.
+    model = Model(inputs, x, name='HRA-baseline_convnet')
+
+    model.compile(optimizer=SGD(lr=0.0001, momentum=0.9),
+                  loss='categorical_crossentropy',
+                  metrics=['accuracy'])
+
+    model.summary()
+
+
+    # load weights
+    if weights == 'HRA' and epochs == 10:
+        if include_top:
+            weights_path = get_file('baseline_model_10_epochs_weights_tf_dim_ordering_tf_kernels.h5',
+                                    WEIGHTS_PATH_10_EPOCHS,
+                                    cache_subdir='hra_models')
+
+    elif weights == 'HRA' and epochs == 20:
+        if include_top:
+            weights_path = get_file('baseline_model_20_epochs_weights_tf_dim_ordering_tf_kernels.h5',
+                                    WEIGHTS_PATH_20_EPOCHS,
+                                    cache_subdir='hra_models')
+
+    elif weights == 'HRA' and epochs == 40:
+        if include_top:
+            weights_path = get_file('baseline_model_40_epochs_weights_tf_dim_ordering_tf_kernels.h5',
+                                    WEIGHTS_PATH_40_EPOCHS,
+                                    cache_subdir='hra_models')
+
+    model.load_weights(weights_path)
+
+    return model
+
+
+if __name__=="__main__":
+
+    model = baseline_model(classes=9, epochs=40, weights='HRA')
+    model.summary()
+