cleanup

diploshic/network.py

@@ -115,76 +115,3 @@ def construct_model(input_shape, domain_adaptation=False, da_weight=1):
         model = Model(inputs=[model_in], outputs=[output])
         model.compile(loss=masked_cce, optimizer="adam", metrics=[masked_categorical_accuracy])
     return model
-
-
-def construct_model_v2(input_shape, domain_adaptation=False, da_weight=1):
-    model_in = Input(input_shape)
-    h = Conv2D(128, 3, activation="relu", padding="same", name="conv1_1")(
-        model_in
-    )
-    h = Conv2D(64, 3, activation="relu", padding="same", name="conv1_2")(h)
-    h = MaxPooling2D(pool_size=3, name="pool1", padding="same")(h)
-    h = Dropout(0.15, name="drop1")(h)
-    h = Flatten(name="flaten1")(h)
-
-    dh = Conv2D(
-        128,
-        2,
-        activation="relu",
-        dilation_rate=[1, 3],
-        padding="same",
-        name="dconv1_1",
-    )(model_in)
-    dh = Conv2D(
-        64,
-        2,
-        activation="relu",
-        dilation_rate=[1, 3],
-        padding="same",
-        name="dconv1_2",
-    )(dh)
-    dh = MaxPooling2D(pool_size=2, name="dpool1")(dh)
-    dh = Dropout(0.15, name="ddrop1")(dh)
-    dh = Flatten(name="dflaten1")(dh)
-
-    dh1 = Conv2D(
-        128,
-        2,
-        activation="relu",
-        dilation_rate=[1, 4],
-        padding="same",
-        name="dconv4_1",
-    )(model_in)
-    dh1 = Conv2D(
-        64,
-        2,
-        activation="relu",
-        dilation_rate=[1, 4],
-        padding="same",
-        name="dconv4_2",
-    )(dh1)
-    dh1 = MaxPooling2D(pool_size=2, name="d1pool1")(dh1)
-    dh1 = Dropout(0.15, name="d1drop1")(dh1)
-    dh1 = Flatten(name="d1flaten1")(dh1)
-
-    h_concated = concatenate([h, dh, dh1])
-    h = Dense(512, name="512dense", activation="relu")(h_concated)
-    h = Dropout(0.2, name="drop7")(h)
-    h = Dense(128, name="last_dense", activation="relu")(h)
-    h = Dropout(0.1, name="drop8")(h)
-    output = Dense(5, name="predictor", activation="softmax")(h)
-    if domain_adaptation:
-        da = GradReverse()(h_concated)
-        da = Dense(512, name="DA512dense", activation="relu")(da)
-        da = Dense(256, name="DA256dense", activation="relu")(da)
-        da = Dense(128, name="DA128dense", activation="relu")(da)
-        domain_output = Dense(1, name="discriminator", activation="sigmoid")(da)
-        model = Model(inputs=[model_in], outputs=[output, domain_output])
-        model.compile(optimizer='adam',
-                        loss={'predictor': masked_cce, 'discriminator': masked_bce},
-                        loss_weights = [1, da_weight], # equal weighing of two tasks
-                        metrics={'predictor': 'accuracy', 'discriminator': 'accuracy'})
-    else:
-        model = Model(inputs=[model_in], outputs=[output])
-        model.compile(loss=masked_cce, optimizer="adam", metrics=["accuracy"])
-    return model