Dimension of shape in conv1D

Question

I have tried to build a CNN with one layer, but I have some problem with it. Indeed, the compilator says me that

ValueError: Error when checking model input: expected conv1d_1_input to have 3 dimensions, but got array with shape (569, 30)

This is the code

import numpy from keras.models import Sequential from keras.layers.convolutional import Conv1D  numpy.random.seed(7)  datasetTraining = numpy.loadtxt("CancerAdapter.csv",delimiter=",") X = datasetTraining[:,1:31] Y = datasetTraining[:,0] datasetTesting = numpy.loadtxt("CancereEvaluation.csv",delimiter=",") X_test = datasetTraining[:,1:31] Y_test = datasetTraining[:,0]  model = Sequential() model.add(Conv1D(2,2,activation='relu',input_shape=X.shape)) model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) model.fit(X, Y, epochs=150, batch_size=5) scores = model.evaluate(X_test, Y_test)  print("\n%s: %.2f%%" % (model.metrics_names[1], scores[1]*100)) 

Answer 1

td; lr you need to reshape you data to have a spatial dimension for Conv1d to make sense:

X = np.expand_dims(X, axis=2) # reshape (569, 30) to (569, 30, 1)  # now input can be set as  model.add(Conv1D(2,2,activation='relu',input_shape=(30, 1)) 

Essentially reshaping a dataset that looks like this:

features     .8, .1, .3   .2, .4, .6   .7, .2, .1   

To:

[[.8 .1 .3],  [.2,  .4,  .6  ],  [.7,  .2,  .1]]   

Explanation and examples

Normally convolution works over spatial dimensions. The kernel is "convolved" over the dimension producing a tensor. In the case of Conv1D, the kernel is passed over the 'steps' dimension of every example.

You will see Conv1D used in NLP where steps is a number of words in the sentence (padded to some fixed maximum length). The words would be encoded as vectors of length 4.

Here is an example sentence:

jack   .1   .3   -.52   | is     .05  .8,  -.7    |<--- kernel is `convolving` along this dimension. a      .5   .31  -.2    | boy    .5   .8   -.4   \|/ 

And the way we would set the input to the conv in this case:

maxlen = 4 input_dim = 3 model.add(Conv1D(2,2,activation='relu',input_shape=(maxlen, input_dim)) 

In your case, you will treat the features as the spatial dimensions with each feature having length 1. (see below)

Here would be an example from your dataset

att1   .04    | att2   .05    |  < -- kernel convolving along this dimension att3   .1     |       notice the features have length 1. each att4   .5    \|/      example have these 4 featues. 

And we would set the Conv1D example as:

maxlen = num_features = 4 # this would be 30 in your case input_dim = 1 # since this is the length of _each_ feature (as shown above)  model.add(Conv1D(2,2,activation='relu',input_shape=(maxlen, input_dim)) 

As you see your dataset has to be reshaped in to (569, 30, 1) use:

X = np.expand_dims(X, axis=2) # reshape (569, 30, 1)  # now input can be set as  model.add(Conv1D(2,2,activation='relu',input_shape=(30, 1)) 

Here is a full-fledged example that you can run (I'll use the Functional API)

from keras.models import Model from keras.layers import Conv1D, Dense, MaxPool1D, Flatten, Input import numpy as np  inp =  Input(shape=(5, 1)) conv = Conv1D(filters=2, kernel_size=2)(inp) pool = MaxPool1D(pool_size=2)(conv) flat = Flatten()(pool) dense = Dense(1)(flat) model = Model(inp, dense) model.compile(loss='mse', optimizer='adam')  print(model.summary())  # get some data X = np.expand_dims(np.random.randn(10, 5), axis=2) y = np.random.randn(10, 1)  # fit model model.fit(X, y) 

Answer 2

I have mentioned this in other posts also:

To input a usual feature table data of shape (nrows, ncols) to Conv1d of Keras, following 2 steps are needed:

xtrain.reshape(nrows, ncols, 1) # For conv1d statement:  input_shape = (ncols, 1) 

For example, taking first 4 features of iris dataset:

To see usual format and its shape:

iris_array = np.array(irisdf.iloc[:,:4].values) print(iris_array[:5]) print(iris_array.shape) 

The output shows usual format and its shape:

[[5.1 3.5 1.4 0.2]  [4.9 3.  1.4 0.2]  [4.7 3.2 1.3 0.2]  [4.6 3.1 1.5 0.2]  [5.  3.6 1.4 0.2]]  (150, 4) 

Following code alters the format:

nrows, ncols = iris_array.shape iris_array = iris_array.reshape(nrows, ncols, 1) print(iris_array[:5]) print(iris_array.shape) 

Output of above code data format and its shape:

[[[5.1]   [3.5]   [1.4]   [0.2]]   [[4.9]   [3. ]   [1.4]   [0.2]]   [[4.7]   [3.2]   [1.3]   [0.2]]   [[4.6]   [3.1]   [1.5]   [0.2]]   [[5. ]   [3.6]   [1.4]   [0.2]]]  (150, 4, 1) 

This works well for Conv1d of Keras. For input_shape (4,1) is needed.