--- a
+++ b/model.py
@@ -0,0 +1,57 @@
+model = Sequential()
+
+model.add(Conv2D(64, (3,3),strides = (1,1), input_shape = IMAGE_SIZE + [3],kernel_initializer='glorot_uniform'))
+
+model.add(keras.layers.ELU())
+
+model.add(BatchNormalization())
+
+model.add(Conv2D(64, (3,3),strides = (1,1),kernel_initializer='glorot_uniform'))
+
+model.add(keras.layers.ELU())
+
+model.add(BatchNormalization())
+
+model.add(MaxPool2D(pool_size=(2, 2), strides= (2,2)))
+
+model.add(Conv2D(128, (3,3),strides = (1,1),kernel_initializer='glorot_uniform'))
+
+model.add(keras.layers.ELU())
+
+model.add(BatchNormalization())
+
+model.add(Conv2D(128, (3,3),strides = (1,1),kernel_initializer='glorot_uniform'))
+
+model.add(keras.layers.ELU())
+
+model.add(BatchNormalization())
+
+model.add(MaxPool2D(pool_size=(2, 2), strides= (2,2)))
+
+model.add(Conv2D(256, (3,3),strides = (1,1),kernel_initializer='glorot_uniform'))
+
+model.add(keras.layers.ELU())
+
+model.add(BatchNormalization())
+
+model.add(Conv2D(256, (3,3),strides = (1,1),kernel_initializer='glorot_uniform'))
+
+model.add(keras.layers.ELU())
+
+model.add(BatchNormalization())
+
+model.add(MaxPool2D(pool_size=(2, 2), strides= (2,2)))
+
+model.add(Flatten())
+
+model.add(Dense(2048))
+
+model.add(keras.layers.ELU())
+
+model.add(BatchNormalization())
+
+model.add(Dropout(0.5))
+
+model.add(Dense(7, activation='softmax'))
+
+model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])