Thanks for your reply! Different models show these observation to different degrees. At first the model regarding the slower execution when using the CPU:
normalizer = Normalization(input_shape=[1024,], axis=None)
normalizer.adapt(X_train)
def test_model():
model = keras.Sequential([
normalizer,
Dense(64, activation='relu'),
Dense(8, activation='relu'),
Dense(1)
])
model.compile(loss='mae',
optimizer=tf.keras.optimizers.Adam(0.001),metrics='mae')
return model
test=test_model()
with tf.device('cpu:0'):
History_test=test.fit(X_train,Y_train,batch_size=9,validation_data=(X_test,Y_test),epochs=200)
This training takes 45s in a tensorflow-metal environment, and the Activity monitor shows 70% GPU usage, even though its told not to use the GPU. Running this code in a standard tensorflow environment takes 9 seconds.
Regarding the GPU power drop: I have restarted my Mac today and the issue seems much less pronounced. Previously I have literally seen a 50% drop, now I see something in the range of about 20%, which makes it far less of a problem. Nonetheless, here is the code and power figures:
input1 = Input((1024,),name='input1')
input2 = Input((1024,),name='input2')
input_shape=(16,217,1)
input3 = Input((input_shape),name='input3')
norm1=layers.LayerNormalization()(input1)
norm2=layers.LayerNormalization()(input2)
dense21= Dense(8,activation='relu')(norm1)
dense22= Dense(8,activation='relu')(norm2)
dense31=Dense(32,activation='relu')(dense21)
dense32=Dense(32,activation='relu')(dense22)
conv_1=Conv2D(64, (3,3), activation='relu', padding="same",input_shape=input_shape)(input3)
maxp_1 = MaxPooling2D(pool_size = (2,2)) (conv_1)
conv_2=Conv2D(128, (3,3), activation='relu', padding="same")(maxp_1)
maxp_2 = MaxPooling2D(pool_size = (2,2)) (conv_2)
conv_2=Conv2D(128, (3,3), activation='relu', padding="same")(maxp_2)
flatten=Flatten()(conv_2)
densePL_1= Dense(128, activation='relu')(flatten)
output= Dense(1, activation='relu')(densePL_1)
concat = layers.concatenate([dense31,dense32,densePL_1])
output_2= Dense(1,activation="relu",name='pred')(concat)
model_concat_test = Model(inputs=[input1,input2,input3], outputs=[output_2])
model_concat_test.compile(loss=["mae"], optimizer="adam",metrics=["mae"])
Historytest=model_concat_test.fit({"input3":X3_train,"input1":X1_train,"input2":X1_train}, Y_train,batch_size=9,validation_data=({"input3":X3_test,"input1":X1_test,"input2":X2_test},Y_test),epochs=500)
Batchsize - GPU Power:
7 - 7W,
8 - 8W,
9 - 11W,
10 - 8W,
11 - 10W,
12 - 10W
