keras model

Author: jCrompton

keras_simple_model.py

@@ -0,0 +1,42 @@
+from keras.layers import Input, Dense, Activation, ZeroPadding2D, BatchNormalization, Flatten, Conv2D
+# Kept the unused imports for future reference
+from keras.layers import AveragePooling2D, MaxPooling2D, Dropout, GlobalMaxPooling2D, GlobalAveragePooling2D
+from keras.models import Model
+import keras.backend as K
+
+K.set_image_data_format('channels_last')
+
+
+def cnn_model(input_shape, show_table=False):
+    """
+    Implementation of the HappyModel.
+
+    Arguments:
+    input_shape -- shape of the images of the dataset
+
+    Returns:
+    model -- a Model() instance in Keras
+    """
+
+    X_input = Input(input_shape)
+    X = ZeroPadding2D((3, 3))(X_input)
+
+    X = Conv2D(32, (7, 7), strides=(1, 1), name='conv0')(X)
+    X = BatchNormalization(axis=3, name='bn0')(X)
+    X = Activation('relu')(X)
+    X = MaxPooling2D((2, 2), name='max_pool')(X)
+
+    X = Flatten()(X)
+    X = Dense(1, activation='sigmoid', name='fc')(X)
+
+    model = Model(inputs=X_input, outputs=X, name='HappyModel')
+
+    if show_table:
+        model.summary()
+
+    return model
+
+
+def show_model_graph(model_instance, file_name):
+    plot_model(model_instance, to_file=file_name)
+    SVG(model_to_dot(model_instance).create(prog='dot', format='svg'))