I have regression problem, where there are around 20 features, the expected output is prices(in float).
Can I use Convolutional neural network here to predict the prices. I used both 1D,2D convolution.
But I get below errors,
For 2D,error is
ValueError: Input 0 of layer sequential_4 is incompatible with the layer: : expected min_ndim=4, found ndim=2. Full shape received: (None, 18)
For 1D, error is
ValueError: Input 0 of layer sequential_4 is incompatible with the layer: : expected min_ndim=3, found ndim=2. Full shape received: (None, 18)
Can I use CNN for data other than images? What I am missing here. Please help here.
Below is the code,
model2 = tf.keras.Sequential()
model2.add(tf.keras.layers.Conv1D(32,kernel_size=(3),strides=1, activation='relu'))
model2.add(tf.keras.layers.BatchNormalization())
model2.add(tf.keras.layers.Conv1D(64, kernel_size=(3), strides=(2)))
model2.add(tf.keras.layers.ReLU())
model2.add(tf.keras.layers.BatchNormalization())
model2.add(tf.keras.layers.Dense(1, activation='linear'))
model2.compile(optimizer='adam',loss='mean_absolute_error',metrics['mean_absolute_error'])
I found the issue. There was issue in model.fit. Instead of calling model.fit(X_train,y_train,val=(X_test,y_test)). I had called in this way model.fit((X_train,y_train), val=(X_test,y_Test)). Model was trying to call it as fit_generator instead of fit because of the extra bracket.
Related
I am trying to build a neural network using Keras but am getting the error:
ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 25168 but received input with shape (None, 34783)
I defined the model to be:
model = Sequential()
model.add(Dense(1024, input_dim = len(X), activation = 'relu'))
model.add(Dense(6, activation='softmax'))
In this, X is the result of using scikit-learn it's CountVectorizer() (after it is trained) as follows:
X = count_vectorizer.transform(X).todense()
Is there any method to fix this? Looking around I found that I might need to reshape the data, however I have no idea how and where.
You are using as input_dim the sample dimensionality: len(X) (the same as X.shape[0]) which is wrong.
Keras expects as input the number of dimensions of the features which, in your case of 2D input, is X.shape[-1]
I have the following (part of) network architecture:
Obtained by
...
pool = GlobalAvgPool()(gc_2)
predictions = Dense(units=32, activation='relu', use_bias=False)(pool)
predictions = BatchNormalization()(predictions)
...
I am trying to insert a batch normalization layer, but I get the following error:
ValueError: Input 0 of layer batch_normalization_1 is incompatible with the layer: expected ndim=2, found ndim=3. Full shape received: [None, 1, 32]
I am guessing the second dimension is causing this mishap. Is there any way I can get rid of it?
If your model is complied successfully, there is no problem with your model definition.
This is more likely to happen because of the input data shape and dimensions are incompatible with your model's desired input shape.
expected ndim=2, found ndim=3. means that the model requires a 2D tensor with
I am facing floating point resolution loss during convolution operation while porting the code on my embedded processor which supports only half precision, so I want to test the intermediate operations that are performed layer by layer in my Keras based model which is performing good while on Full precision on my desktop.
In the following snippet of code I want to compute the 1DConv on the 1500x3 shaped input data. The kernel size is 10 and Kernel shape is (10x3x16).
To compute the 1D-Convolution, Keras does the Expand Dimensions on input shape and add one more dimension to it, which becomes suitable for 2D Convolution operation.
Then series of operations are called e.g. Conv2D followed by Squeeze and finally BiasAdd.
Finally the output of the Conv1D layer is pushed in
conv1d_20/Elu layer.
Please find the picture attached for full description of operations involved.
Now, I want to test the output much before the actual output of a Layer is produced.
Please see the below code:
Input_sequence = keras.layers.Input(shape=(1500,3))
encoder_conv1 = keras.layers.Conv1D(filters=16, kernel_size=10, padding='same', activation=tf.nn.elu)(Input_sequence)
The Model summary shows:
Model: "model_5"
_________________________________________________________________
Layer (type) Output Shape Param #
=================================================================
input_5 (InputLayer) [(None, 1500, 3)] 0
_________________________________________________________________
conv1d_20 (Conv1D) (None, 1500, 16) 496
I want to define the model output at conv1d_20/Conv2D but it gives me error. But the below is accepted at compilation.
encoder = keras.Model(inputs=autoencoder.input, outputs=autoencoder.get_layer('conv1d_20').output)
encoder.get_output_at(0)
It outputs
<tf.Tensor 'conv1d_20/Elu:0' shape=(?, 1500, 16) dtype=float32>
I want to test the output of Conv2D operation but it produces the output of conv1d_20/Elu.
How can I do this test. Please help me.
Conv1D operation
You can disable the bias(use_bias=False) and activation functions(activation=None) when defining the Conv1D operation.
Input_sequence = keras.layers.Input(shape=(1500,3))
encoder_conv1 = keras.layers.Conv1D(filters=16, kernel_size=10,
padding='same', use_bias=False,
activation=None)(Input_sequence)
I have a model with conv1d as the first layer.
My data is time series data where each sample consists of 41 time steps where each time step has 4 features.
I have about 1000 samples.
I have specified the input shape of the conve1d layer to be (41,4) as it supposed to be.
However, I keep getting the following error: Input 0 is incompatible with layer conv1d_48: expected ndim=3, found ndim=2.
I suspect that the problem is that the shape of X is (1000,) while the shape of X[0] is (41,4). Has anyone encountered this problem?
Thanks.
l1=Input(shape=(41,4))
x=Conv1D(64,(4))(l1)
x=GlobalMaxPooling1D()(x)
x=Dense(1)(x)
model=Model(l1,x)
model.compile('rmsprop','binary_crossentropy',metrics=['acc'])
model.fit(X,y,32,10)
You defined an expected input on your Conv1D to be be 2D -> (41, 4)
But you give to it an input of shape (41,), be consistant in your definitions !
If you specify the input_shape in your Conv1D layer, you don't need to feed an Input layer to it.
Or you can change the shape of this Input layer to be consistant with this input_shape.
I have the following network:
model = Sequential()
model.add(Embedding(400000, 100, weights=[emb], input_length=12, trainable=False))
model.add(Conv2D(256,(2,2),activation='relu'))
the output from the embedding layer is of shape (batchSize, 12, 100). The conv2D layer requires an input of shape (batchSize, filter, 12, 100), and I get the following error:
Input 0 is incompatible with layer conv2d_1: expected ndim=4, found ndim=3
So, how can I expand the output from the embedding layer to make it proper for the Conv2D layer?
I'm using Keras with Tensorflow as the back end.
Adding a reshape Layer should be the way to go https://keras.io/layers/core/#reshape
Depending on the concrete situation Conv1D cold although work.
I managed to add another dimension with the following piece of code:
model = Sequential()
model.add(Embedding(400000, 100, weights=[emb], input_length=12, trainable=False))
model.add(Lambda(lambda x: expand_dims(x, 3)))
model.add(Conv2D(256,(2,2),activation='relu'))