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CNN/lenet/README.md

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+# LeNet - Handwritten Digit Recognition
+
+## Description
+
+This model predicts handwritten digits using a convolutional neural network (CNN).
+
+## Model
+
+### Dataset
+
+This model has been trained on the popular [MNIST datset](http://yann.lecun.com/exdb/mnist/), imported directly form keras.datasets
+

CNN/lenet/gen_scripts/lenet_keras2.2.4.py

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+import numpy as np
+import pandas as pd
+from time import time
+from sklearn.model_selection import train_test_split
+import keras
+import keras.layers as layers
+from keras import optimizers
+from keras.preprocessing.image import ImageDataGenerator
+from keras.models import Model
+from keras.layers import Input, Activation, Concatenate
+from keras.layers import Flatten, Dropout
+from keras.layers import Convolution2D, MaxPooling2D
+from keras.layers import GlobalAveragePooling2D
+
+# Training variables
+EPOCHS = 30
+BATCH_SIZE = 480
+
+# dataset
+def prepare_mnist_data(rows=28, cols=28, nb_classes=10, categorical=False, padding = True, debug = True):
+    """ Get MNIST data """
+
+    from keras.datasets import mnist
+    from keras.utils import np_utils
+    (X_train, y_train), (X_test, y_test) = mnist.load_data()
+    X_train = X_train.reshape(X_train.shape[0], rows, cols, 1)
+    X_test = X_test.reshape(X_test.shape[0], rows, cols, 1)
+    X_train = X_train.astype('float32')
+    X_test = X_test.astype('float32')
+    X_train /= 255
+    X_test /= 255
+    X_train, X_validation, y_train, y_validation = train_test_split(X_train, y_train, test_size=0.2, random_state=0)
+    if padding:
+    # Pad images with 0s
+        X_train      = np.pad(X_train, ((0,0),(2,2),(2,2),(0,0)), 'constant')
+        X_test       = np.pad(X_test, ((0,0),(2,2),(2,2),(0,0)), 'constant')
+        X_validation        = np.pad(X_validation, ((0,0),(2,2),(2,2),(0,0)), 'constant')
+
+    if categorical:
+    # convert class vectors to binary class matrices
+        y_train = np_utils.to_categorical(y_train, nb_classes)
+        y_test = np_utils.to_categorical(y_test, nb_classes)
+        y_validation = np_utils.to_categorical(y_validation, nb_classes)
+
+    if debug:
+        print('X_train shape:', X_train.shape)
+        print(X_train.shape[0], 'train samples')
+        print(X_test.shape[0], 'test samples')
+        print(X_validation.shape[0], 'validation samples')
+        if not categorical:
+            train_labels_count = np.unique(y_train, return_counts=True)
+            dataframe_train_labels = pd.DataFrame({'Label':train_labels_count[0], 'Count':train_labels_count[1]})
+            print(dataframe_train_labels)
+
+    return X_train, X_test, X_validation, y_train, y_test, y_validation
+
+
+# SqueezeNet
+def LeNet():
+    model = keras.Sequential()
+
+    model.add(layers.Conv2D(filters=6, kernel_size=(3, 3), activation='relu', input_shape=(32,32,1)))
+    model.add(layers.AveragePooling2D())
+
+    model.add(layers.Conv2D(filters=16, kernel_size=(3, 3), activation='relu'))
+    model.add(layers.AveragePooling2D())
+
+    model.add(layers.Flatten())
+
+    model.add(layers.Dense(units=120, activation='relu'))
+
+    model.add(layers.Dense(units=84, activation='relu'))
+
+    model.add(layers.Dense(units=10, activation = 'softmax'))
+    return model
+
+# Get dataset
+X_train, X_test, X_validation, y_train, y_test, y_validation = prepare_mnist_data(categorical = True, debug=True)
+
+# Check
+model = LeNet()
+model.summary()
+
+# Preparation
+model.compile(loss=keras.losses.categorical_crossentropy, optimizer=keras.optimizers.Adam(), metrics=['accuracy'])
+
+# Trainer and validator
+train_generator = ImageDataGenerator().flow(X_train, y_train, batch_size=BATCH_SIZE)
+validation_generator = ImageDataGenerator().flow(X_validation, y_validation, batch_size=BATCH_SIZE)
+
+# Training
+from keras.callbacks import TensorBoard
+steps_per_epoch = X_train.shape[0]//BATCH_SIZE
+validation_steps = X_validation.shape[0]//BATCH_SIZE
+tensorboard = TensorBoard(log_dir="logs/{}".format(time()))
+
+model.fit_generator(train_generator, steps_per_epoch=steps_per_epoch, epochs=EPOCHS, 
+                    validation_data=validation_generator, validation_steps=validation_steps, 
+                    shuffle=True, callbacks=[tensorboard])
+
+# Saving the model in keras format (.h5)
+model.save('lenet_keras_v2.2.4.h5')
+print(">>> Model saved!")