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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.

How to solve the dimension problem for SpatialPyramidPooling2D

I'm currently working with the TensorFlow Addons SpatialPyramidPooling2D layer for image classification and I got the following error when I tried to fit the model.
ValueError: Dimensions must be equal, but are 8 and 20 for '{{node MatMul}} = BatchMatMulV2[T=DT_FLOAT, adj_x=false, adj_y=false](feature, transpose_1)' with input shapes: [?,20,8], [8,20,?]
I doubt that it's something to do with the output shape of the model. The last layer is supposed to be (None,<number_of_classes>) but I got (None,<number_of_channels>,<number_of_classes>). Because the output of SpatialPyraidPooling2D is a 3D tensor.
I tried to solve it by adding a Flatten layer right after SpatialPyramidPooling2D but it ends up the softmax layer giving me an error as below
ValueError: Input 0 of layer dense is incompatible with the layer: expected axis -1 of input shape to have value 1280 but received input with shape [None, 25600]

If you want output of shape (None, 8), I suggest you add a 1D pooling layer after the pyramid pooling thing.
import tensorflow as tf
x = tf.random.uniform((10, 20, 8), dtype=tf.float32)
pool = tf.keras.layers.GlobalAveragePooling1D()
print(pool(x).shape)
TensorShape([10, 8])

Building/Training 1D CNN for sequence-InvalidArgument

I'm building and training a CNN for a sequence, and have been using RNN's successfully, but am running into issues with CNN.
Here's the code, cnn1 is first (more complex model), tried getting a simpler one to fit and getting errors on both:
The shapes are as follows:
xtrain (5206, 19, 4)
ytrain (5206, 4)
xvalid (651, 19, 4)
yvalid (651, 4)
xtest (651, 19, 4)
ytest (651, 4)
I've tried just about every combination of kernel sizes and nodes I can think of, tried 2 different model builds.
model_cnn1.add(keras.layers.Conv1D(32, (4), activation='relu'))
model_cnn1.add(keras.layers.MaxPooling1D((4)))
model_cnn1.add(keras.layers.Conv1D(32, (4), activation='relu'))
model_cnn1.add(keras.layers.MaxPooling1D((4)))
model_cnn1.add(keras.layers.Conv1D(32, (4), activation='relu'))
model_cnn1.add(keras.layers.Dense(4))
model_cnn2 = keras.models.Sequential([
keras.layers.Conv1D(100,(4),input_shape=(19,4),activation='relu'),
keras.layers.MaxPooling1D(4),
keras.layers.Dense(4)
])
model_cnn2.compile(loss='mse',optimizer='adam',metrics= ['mse','accuracy'])
model_cnn2.fit(X_train_tf,y_train_tf,epochs=25)
Output is 1/25 epochs, not entirely run, then on cnn1 I receive some variation of (final line):
ValueError: Negative dimension size caused by subtracting 4 from 1 for
'max_pooling1d_26/MaxPool' (op: 'MaxPool') with input shapes:
[?,1,1,32]
on cnn2 (simpler) I get error (final line):
InvalidArgumentError: Incompatible shapes: [32,4,4] vs. [32,4]
[[{{node metrics_6/mse/SquaredDifference}}]]
[Op:__inference_keras_scratch_graph_6917]
In general, is there some rule I should be following here for kernels/nodes/etc? I always seem to get these errors on the shape.
I'm hoping after I build a model of each type I'll understand the ins and outs--no pun intended--but it's driving me crazy!
I've tried every combination of

You can read up on the docs of the Conv1D and MaxPooling1D to read that these layers change the output shape depending on the value for strides. In your case you can keep the output shape for Conv1D equal by specifying a padding. MaxPooling1D changes the output shape by definition. With strides = 4, the output shape will be 4 times smaller in fact. I'd suggest carefully reading the docs to figure out exactly what happens and learning about the underlying theory of CNNs as to why this happens.

python, neural network, dimension and value of input_shape to be used

i need to build a CNN model on a dataset which has 65536 rows (represent 1 image for each), 49 columns (7x7 image) and binary class(50th column).
I am referencing examples on performing CNN using mnist dataset but i failed to build the train model.
When i'm on this line of code:
model.add(Conv2D(30 ,(5,5), padding='valid', activation='relu',input_shape=(1,7,7))
i am have this error :
ValueError: Negative dimension size caused by subtracting 5 from 1 for 'conv2d_42/convolution' (op: 'Conv2D') with input shapes: [?,1,7,7], [5,5,7,30].
where i try this :
model.add(Conv2D(30 ,(5,5), padding='valid', activation='relu',input_shape=(1(7,7)))
I had this :
TypeError: int() argument must be a string or a number, not 'tuple'
what im asking is what value of input_shape i should be using to build the model

As you know, Keras can run on top of either Theano of Tensorflow. You are using Theano dimension ordering (channels, height, width) but your Keras seems to be using Tensorflow backend, with Tensorflow dimension ordering (height, width, channels).
I would suggest rewriting the code and put channel dimension (=1) last. There are also way of changing backend and/or dimension ordering by editing the keras.json.