CNN 架构问题

Question

I'm trying to implement a CNN architecture, but, there are an issue with the shapes of the output. The shapes of the sets are as follow:

x_train.shape, y_train.shape, x_test.shape, y_test.shape

((1203, 162, 1), (1203, 7), (402, 162, 1), (402, 7))

The architecture's setting is as follows:

input_x = tf.keras.layers.Input(shape = (x_train.shape[1],1))
conv_1 = tf.keras.layers.Conv1D(filters=16,kernel_size=3,padding="same",activation="relu")(input_x)
pool_1 = tf.keras.layers.MaxPooling1D(2)(conv_1)
conv_2 = tf.keras.layers.Conv1D(filters=32,kernel_size=3,padding="same",activation="relu")(pool_1)
pool_2  = tf.keras.layers.MaxPooling1D(2)(conv_2)

flatten = tf.keras.layers.Flatten()(pool_2)
dense = tf.keras.layers.Dense(512, activation="relu")(flatten)
fb = tf.keras.layers.Dropout(0.4)(dense)
fb = tf.keras.layers.Dense(512, activation="relu")(fb)
fb = tf.keras.layers.Dropout(0.4)(fb)

output = tf.keras.layers.Dense(8, activation="softmax")(fb)
model_branching_summed = tf.keras.models.Model(inputs=input_x, outputs=output)
model_branching_summed.summary()
model_branching_summed.compile(optimizer=SGD(learning_rate=0.01 , momentum=0.8), loss='categorical_crossentropy', metrics= ['accuracy'])

history=model_branching_summed.fit(x_train, y_train, batch_size=128, epochs=100, validation_data=(x_test, y_test), callbacks=[rlrp])

But when I run the model, it give me the follow error:

ValueError Traceback (most recent call last) Cell In[192], line 5 1 rlrp = ReduceLROnPlateau(monitor='loss', factor=0.4, verbose=0, patience=2,min_lr=0.0001) 2 #(min_lr=0.000001) ----> 5 history=model_branching_summed.fit(x_train, y_train, batch_size=128, epochs=100, validation_data=(x_test, y_test), callbacks=[rlrp])

ValueError: Shapes (None, 7) and (None, 8) are incompatible

Can someone help me to know where is the error?

Answer 1

You seen to have an output layer in the shape of (None, 8) but you are trying to calculate loss on a y_train matrix of shape (None, 7).

Try changing this line:

output = tf.keras.layers.Dense(8, activation="softmax")(fb)

to

output = tf.keras.layers.Dense(7, activation="softmax")(fb)