We Keep Coding

How is the Keras Conv1D input specified? I seem to be lacking a dimension

My input is a array of 64 integers.
model = Sequential()
model.add( Input(shape=(68,), name="input"))
model.add(Conv1D(64, 2, activation="relu", padding="same", name="convLayer"))
I have 10,000 of these arrays in my training set. And I supposed to be specifying this in order for conv1D to work?
I am getting the dreaded
ValueError: Input 0 of layer convLayer is incompatible with the layer: : expected min_ndim=3, found ndim=2. Full shape received: [None, 68]
error and I really don't understand what I need to do.

Don't let the name confuse you. The layer tf.keras.layers.Conv1D needs the following shape: (time_steps, features). If your dataset is made of 10,000 samples with each sample having 64 values, then your data has the shape (10000, 64), which is not directly applicable to the tf.keras.layers.Conv1D layer. You are missing the time_steps dimension. What you can do is use the tf.keras.layers.RepeatVector, which repeats your array input n times, in the example 5. This way your Conv1D layer gets an input of the shape (5, 64). Check out the documentation for more information:
time_steps = 5
model = tf.keras.Sequential()
model.add(tf.keras.layers.Input(shape=(64,), name="input"))
model.add(tf.keras.layers.RepeatVector(time_steps))
model.add(tf.keras.layers.Conv1D(64, 2, activation="relu", padding="same", name="convLayer"))
As a side note, you should ask yourself if using a tf.keras.layers.Conv1D layer is the right option for your use case. This layer is usually used for NLP and other time series tasks. For example, in sentence classification, each word in a sentence is usually mapped to a high-dimensional word vector representation, as seen in the image. This results in data with the shape (time_steps, features).
                                          
If you want to use character one hot encoded embeddings it would look something like this:
                                          
This is a simple example of one single sample with the shape (10, 10) --> 10 characters along the time series dimension and 10 features. It should help you understand the tutorial I mentioned a bit better.

The Conv1D layer does temporal convolution, that is, along the first dimension (not the batch dimension of course), so you should put something like this:
time_steps = 5
model = tf.keras.Sequential()
model.add(tf.keras.layers.Input(shape=(time_steps, 64), name="input"))
model.add(tf.keras.layers.Conv1D(64, 2, activation="relu", padding="same", name="convLayer"))
You will need to slice your data into time_steps temporal slices to feed the network.
However, if your arrays don't have a temporal structure, then conv1D is not the layer you are looking for.

I am trying Time series forecasting using CNN , LSTM and MLP. But when i use TimeDistributed with CNN it gives dimensionality error, during fitting

I have 9 features, one output variable i.e. to be predicted, window size is 5
code works very well without "TimeDistributed" command
MODEL INPUT SHAPE: feature_tensor.shape=(1649, 5, 9) MODEL OUTPUT
SHAPE: y_train.shape= (1649,)
Thats my Code:
#Build the network model
act_fn='relu'
modelq = Sequential()
modelq.add(TimeDistributed(Conv1D(filters=105, kernel_size=2, activation=act_fn, input_shape=(None, feature_tensor.shape[1],feature_tensor.shape[2]))))
modelq.add(TimeDistributed(AveragePooling1D(pool_size=1)))
modelq.add(TimeDistributed(Flatten()))
modelq.add(LSTM(50))
modelq.add(Dense(64, activation=act_fn))
modelq.add(Dense(1))
#Compile the model
modelq.compile(optimizer='adam', loss='mean_squared_error')
modelq.fit(feature_tensor, y_train ,batch_size=1, epochs=epoch_count)
THE ERROR STATEMENT IS :
ValueError: Input 0 of layer conv1d is incompatible with the layer: : expected min_ndim=3, found ndim=2. Full shape received: (5, 9)
I feel like there is some thing wrong with dimensionality of "feature_tensor" during "Model FITTING" i.e last command... But I don't know what's wrong with it :(

Your intuition is right, the problem is the dimensionality of tensor_feature. If you take a look in the documentation of TimeDistributed you see an example with images and Conv2d layers. There the the input has to have the following shape: batch_size, time steps, x_dim, y_dim, channels. Since you use time-series you need: batch_size, time steps, 1, features. E.g. you can reshape your data by numpy:
feature_tensor = np.reshape(feature_tensor, (-1, 5, 1, 9))
However, I am not sure if it useful to combine Conv1D with TimeDistributed, since in that case you apply the convolution only on the features and not on temporal contiguous values, where a 1d Convolution should be applied.

Error when checking input in first layer of Keras Conv1D

I'm trying to assign one of two classes (positive/nagative) to audio using CNN with Keras. My model should accept varied lengths of input (frames) in which each frame contains 41 features but I struggle with the input size. Bear in mind that I haven't acquired full dataset so I just mocked some meaningless data just to check if network works at all.
According to documentation https://keras.io/layers/convolutional/ and my best understanding Conv1D can tackle varied lengths if first element of input_shape tuple is None. Shape of variable containing input data X_train.shape is (4, 497, 41).
data = pd.read_csv('output_file.csv', sep=';')
featureCount = data.values.shape[1]
#mocks because full data is not available yet
Y_train = np.asarray([1, 0, 1, 0])
X_train = np.asarray(
[np.array(data.values, copy=True), np.array(data.values, copy=True), np.array(data.values, copy=True),
np.array(data.values, copy=True)])
# variable length with 41 features
model = keras.models.Sequential()
model.add(keras.layers.Conv1D(100, 5, activation='relu', input_shape=(None, featureCount)))
model.add(keras.layers.GlobalMaxPooling1D())
model.add(keras.layers.Dense(10, activation='relu'))
model.add(keras.layers.Dense(1, activation='sigmoid'))
model.compile(optimizer='adam',
loss='binary_crossentropy',
metrics=['accuracy'])
model.summary()
model.fit(X_train, Y_train, epochs=10, verbose=False, validation_data=(np.array(data.values, copy=True), [1]))
This code produces error
ValueError: Error when checking input: expected conv1d_input to have 3 dimensions, but got array with shape (497, 41). So it appears like the first dimension was cut out as it contains training samples (it seems correct to me) what bothered me is the required dimensionality, why is it 3?
After searching for the answer I stumbled onto Dimension of shape in conv1D and followed it by adding last dimension (using X_train = np.expand_dims(X_train, axis=3)) that contains only single digit but I ended up with another, similar error:
ValueError: Error when checking input: expected conv1d_input to have 3 dimensions, but got array with shape (4, 497, 41, 1) now it seems that first dimension that previously was treated as sample "list" is now part of actual data.
I also tried fiddling with input_shape parameter but to no avail and using Reshape layer but ended up fighting with size the
What should I do to satisfy required shape? How to prepare data for processing?

Input Format for Mulivariate Time Series Binary Classification

I am trying to use an LSTM model for binary classification on multivariate time series data. I have seven properties collected over the course of the day for about 100 days (i.e. 100 arrays of size [9000, 7]). Each of these arrays has a single classification status of either 1 or 0.
I've started out trying to build the simplest model possible given that I am new to Keras and Machine Learning in general, but I keep getting errors regarding input shape when I try to train them. For example, my first layers:
model = Sequential()
model.add(Conv2D(32, (3,3), input_shape=(9000,7,1), activation='relu'))
...
model.fit(x=X_train, y=Y_train, epochs=100)
With an X_train of type float64, and a size of (100L, 9000L, 7L), I get an error that reads:
ValueError: Error when checking input: expected conv2d_11_input to have 4 dimensions, but got array with shape (100L, 9000L, 7L)
I've tried changing the batch size and number of epochs with no sucess so can someone explain how to correctly reshape my input? Am I missing something simple?

I suspect you want to use a Conv1D (3D data), no?
You're using a Conv2D (4D data = images).
For either Conv1D and any of the RNN layers, such as LSTM, your input is 3D data and your input_shape should be input_shape=(9000,7).
The input data should be an array with shape (100,9000,7), which is already ok, by the content of the error message.
Assuming each day is an individual sequence and you don't want to connect days.

How do I properly deal with 1 dimensional input in Keras Conv1D?

So I'm trying to train a model in Keras that takes in frames of signals that are of a shape (750,1). My first layer is the following Conv1D layer:
Conv1D(128, 5,input_shape=(1,750) padding='valid', activation='relu', strides=1)
But this gives me the following error:
ValueError: Negative dimension size caused by subtracting 5 from 1 for 'conv1d_1/convolution/Conv2D' (op: 'Conv2D') with input shapes: [?,1,1,750], [1,5,750,128].
Which seems to indicate that the layer is trying to apply a 5x5 kernel to the 1 dimensional data which doesn't make much sense. Any other input shapes just seem to throw different less useful errors. What am I doing wrong? Am I completely misunderstanding Conv1D ?