(img_dim, nb_classes, model_name="CNN")
| 40 | |
| 41 | |
| 42 | def CNN(img_dim, nb_classes, model_name="CNN"): |
| 43 | |
| 44 | x_input = Input(shape=img_dim, name="input") |
| 45 | |
| 46 | x = standard_conv_block(x_input, 32) |
| 47 | x = standard_conv_block(x, 32, pooling=True, dropout_rate=0.25) |
| 48 | x = standard_conv_block(x, 64) |
| 49 | x = standard_conv_block(x, 64, pooling=True, dropout_rate=0.25) |
| 50 | |
| 51 | # FC part |
| 52 | x = Flatten()(x) |
| 53 | x = Dense(512, activation="relu")(x) |
| 54 | x = Dense(nb_classes, activation="softmax")(x) |
| 55 | |
| 56 | CNN = Model(inputs=[x_input], outputs=[x]) |
| 57 | CNN.name = model_name |
| 58 | |
| 59 | return CNN |
| 60 | |
| 61 | |
| 62 | def Big_CNN(img_dim, nb_classes, model_name="Big_CNN"): |
no test coverage detected