CODEWITHSUNDEEP
machine-learningkerasdeep-learningconv-neural-network
Rishavv
Rishavv

Reputation: 293

Dimensionality error while using CNN conv1d model

I have a dataset where x_train shape is (34650,10,1) , y_train shape is (34650,) , x_test shape is (17067,10,1) and y_test is (17067,) .

I am making a simple cnn model -

input_layer = Input(shape=(10, 1))
conv2 = Conv1D(filters=64,
               kernel_size=3,
               strides=1,
               activation='relu')(input_layer)
pool1 = MaxPooling1D(pool_size=1)(conv2)
drop1 = Dropout(0.5)(pool1)
pool2 = MaxPooling1D(pool_size=1)(drop1)
conv3 = Conv1D(filters=64,
               kernel_size=3,
               strides=1,
               activation='relu')(pool2)
drop2 = Dropout(0.5)(conv3)
conv4 = Conv1D(filters=64,
               kernel_size=3,
               strides=1,
               activation='relu')(drop2)
pool3 = MaxPooling1D(pool_size=1)(conv4)
conv5 = Conv1D(filters=64,
               kernel_size=3,
               strides=1,
               activation='relu')(pool3)
output_layer = Dense(1, activation='sigmoid')(conv5)
model_2 = Model(inputs=input_layer, outputs=output_layer)

But when i am trying to fit the model

model_2.compile(loss='mse',optimizer='adam')
model_2 = model_2.fit(x_train, y_train,
          batch_size=128,
          epochs=2,
          verbose=1,
          validation_data=(x_test, y_test))

I am getting this error

---------------------------------------------------------------------------
ValueError                                Traceback (most recent call last)
<ipython-input-177-aee9b3241a20> in <module>()
      4           epochs=2,
      5           verbose=1,
----> 6           validation_data=(x_test, y_test))

2 frames
/usr/local/lib/python3.6/dist-packages/keras/engine/training_utils.py in standardize_input_data(data, names, shapes, check_batch_axis, exception_prefix)
    133                         ': expected ' + names[i] + ' to have ' +
    134                         str(len(shape)) + ' dimensions, but got array '
--> 135                         'with shape ' + str(data_shape))
    136                 if not check_batch_axis:
    137                     data_shape = data_shape[1:]

ValueError: Error when checking target: expected dense_14 to have 3 dimensions, but got array with shape (34650, 1)

The shape of x_train and x_test is already 3 dimensional, then why it is showing this error

Upvotes: 2

Views: 57

Answers (1)

Marco Cerliani
Marco Cerliani

Reputation: 22021

this is because your input is 3d and your target is 2d. Inside your network there isn't anything that enables you to pass from to 3d to 2d. to do this you can use global pooling or flatten. below an example

n_sample = 100
X = np.random.uniform(0,1, (n_sample,10,1))
y = np.random.randint(0,2, n_sample)

input_layer = Input(shape=(10, 1))
conv2 = Conv1D(filters=64,
               kernel_size=3,
               strides=1,
               activation='relu')(input_layer)
pool1 = MaxPooling1D(pool_size=1)(conv2)
drop1 = Dropout(0.5)(pool1)
pool2 = MaxPooling1D(pool_size=1)(drop1)
conv3 = Conv1D(filters=64,
               kernel_size=3,
               strides=1,
               activation='relu')(pool2)
drop2 = Dropout(0.5)(conv3)
conv4 = Conv1D(filters=64,
               kernel_size=3,
               strides=1,
               activation='relu')(drop2)
pool3 = MaxPooling1D(pool_size=1)(conv4)
conv5 = Conv1D(filters=64,
               kernel_size=3,
               strides=1,
               activation='relu')(pool3)
x = GlobalMaxPool1D()(conv5) # =====> from 3d to 2d (also GlobalAvg1D or Flatten are ok)
output_layer = Dense(1, activation='sigmoid')(x)
model_2 = Model(inputs=input_layer, outputs=output_layer)

model_2.compile('adam', 'binary_crossentropy')
model_2.fit(X,y, epochs=3)

Upvotes: 1

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