mikef0x

mikef0x OP t1_iv0d8up wrote

Reply to comment by BlazeObsidian in [R] Keras image classification high loss by mikef0x

model = tf.keras.models.Sequential()model.add(tf.keras.layers.Conv2D(64, (3, 3), input_shape=(124, 124, 3)))model.add(tf.keras.layers.MaxPooling2D((2, 2)))model.add(tf.keras.layers.Conv2D(32, (3, 3)))model.add(tf.keras.layers.MaxPooling2D((2, 2)))model.add(tf.keras.layers.Conv2D(8, (3, 3)))model.add(tf.keras.layers.Flatten())model.add(tf.keras.layers.Dense(4, activation='softmax'))

so, i've done like this. loss is low now but accuracy is to high :D i mean on epoch 10 its around 0.99.

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update: on 20 epoch: accuracy is 1

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mikef0x OP t1_iv00teb wrote

Reply to comment by BlazeObsidian in [R] Keras image classification high loss by mikef0x

I am beginner at ML. So it would be like this?

model = models.Sequential() model.add(layers.Conv2D(32, (3, 3), activation='relu', input_shape=(32, 32, 3))) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu')) model.add(layers.MaxPooling2D((2, 2))) model.add(layers.Conv2D(64, (3, 3), activation='relu'))

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