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binary signal data: keras ValueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=3, found ndim=2

I'm attempting to train models for RF fingerprinting, and have captured samples from a number of devices at a length of 1 million each. I've converted the samples into a variety of images, and have successfully trained models using that form of data by means of:
imageSize = 224
x_train = np.array(x_train) / 255
x_train.reshape(-1, imageSize, imageSize, 1)
x_val = np.array(x_val) / 255
x_val.reshape(-1, imageSize, imageSize, 1)
y_train = np.array(y_train)
y_val = np.array(y_val)
model = Sequential()
model.add(Conv2D(96, 7, padding="same", activation="relu", input_shape = (224, 224, 3)))
model.add(MaxPool2D())
model.add(Conv2D(96, 7, padding="same", activation="relu"))
model.add(MaxPool2D())
model.add(Conv2D(192, 7, padding="same", activation="relu"))
model.add(MaxPool2D())
model.add(Dropout(0.4))
model.add(Flatten())
model.add(Dense(384, activation="relu"))
model.add(Dense(6, activation="softmax"))
opt = Adam(learning_rate=0.000001)
model.compile(optimizer = opt, loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=["accuracy"])
model.summary()
history = model.fit(x_train, y_train, epochs = 500, validation_data = (x_val, y_val))
However, attempting to do the same to the array data (shape (60, 4000)) which was used to create the images yields the "ValueError: Input 0 of layer sequential is incompatible with the layer: : expected min_ndim=3, found ndim=2" issue listed in the title. My code for that is:
x_train = np.array(x_train)
x_train.reshape(-1, 4000, 1)
x_val = np.array(x_val)
x_val.reshape(-1, 4000, 1)
y_train = np.array(y_train)
y_val = np.array(y_val)
model = Sequential()
model.add(Conv1D(96, 7, padding="same", activation="relu", input_shape=(4000, 1)))
model.add(MaxPooling1D())
model.add(Conv1D(96, 7, padding="same", activation="relu"))
model.add(MaxPooling1D())
model.add(Conv1D(192, 7, padding="same", activation="relu"))
model.add(MaxPooling1D())
model.add(Dropout(0.4))
model.add(Flatten())
model.add(Dense(384, activation="relu"))
model.add(Dense(6, activation="softmax"))
opt = Adam(learning_rate=0.000001)
model.compile(optimizer = opt, loss = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True), metrics=["accuracy"])
model.summary()
history = model.fit(x_train, y_train, epochs = 500, validation_data = (x_val, y_val))
Like many it seems, I'm unable to figure out why this input shape isn't working for the array data. Any clarifications will be helpful.

The error: expected min_ndim=3, found ndim=2 clearly explains all. The first problem, you used input shape is in three dimension (224, 224, 3), while for the second one the input shape changed to 1 dimensional array of shape (4000, 1). You should reshape the dimension of your input to the sequential model.

Keras model ValueError: Can not squeeze dim[1], expected a dimension of 1, got 90

My current model is:
# from tensorflow.keras.layers import InputLayer
model_training = Sequential()
# input_layer = keras.Input(shape=(300,1))
model_training.add(InputLayer(input_shape=(300,1)))
model_training.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='tanh'))
model_training.add(Dropout(0.2))
model_training.add(MaxPooling1D(pool_size=3))
model_training.add(Dropout(0.2))
model_training.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='tanh'))
model_training.add(Dropout(0.2))
model_training.add(MaxPooling1D(pool_size=3))
# model_training.add(Dropout(0.2))
# model_training.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='tanh'))
# model_training.add(Dropout(0.2))
# model_training.add(MaxPooling1D(pool_size=3))
# model_training.add(Dropout(0.2))
# model_training.add(Conv1D(filters=32, kernel_size=3, padding='same', activation='tanh'))
# model_training.add(Dropout(0.2))
# model_training.add(MaxPooling1D(pool_size=3))
# model_training.add(Dropout(0.2))
#model.add(Dropout(0.2))
model_training.add(Flatten())
model_training.add(Dense(90))
model_training.add(Activation('sigmoid'))
model_training.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model_training.summary())
My fit function:
model_training.fit(train_data, train_labels, validation_data=(test_data, test_labels), batch_size=32, epochs=15)
I get this error when I run this:
ValueError: Can not squeeze dim[1], expected a dimension of 1, got 90 for '{{node Squeeze}} = Squeeze[T=DT_FLOAT, squeeze_dims=[-1]](remove_squeezable_dimensions/Squeeze)' with input shapes: [?,90].
Any idea?
my output layer has 90 as there are 90 classes in total to give a prediction on.
The train and labels are shaped as follows:
(7769, 300, 1)
(7769, 90, 1)
I can't figure out this issue. Any help is appreciated!
Partial model summary:

Squeeze your labels before training:
train_labels = tf.squeeze(train_labels, axis=-1)
It seems like the shape of your labels is the problem. The model will output a shape of (batch, 90), but you are providing (batch, 90, 1). Keras is unable to squeeze dimension 1 because it has a length of 90 and not 1.

Input shape error with Keras - not sure what's going on?

This code was given to us by a teacher, so it should work right off the bat. However, I can't get it to run.
K.image_data_format()
# fix random seed for reproducibility
seed = 7
numpy.random.seed(seed)
# load data
(X_train, y_train), (X_test, y_test) = cifar10.load_data()
# normalize inputs from 0-255 to 0.0-1.0
X_train = X_train.astype('float32')
X_test = X_test.astype('float32')
X_train = X_train / 255.0
X_test = X_test / 255.0
# one hot encode outputs
y_train = np_utils.to_categorical(y_train)
y_test = np_utils.to_categorical(y_test)
num_classes = y_test.shape[1]
# Create the model
model = Sequential()
model.add(Conv2D(32, (3, 3), input_shape=(3, 32, 32), padding='same', activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.2))
model.add(Conv2D(32, (3, 3), activation='relu', padding='same', kernel_constraint=maxnorm(3)))
model.add(MaxPooling2D(pool_size=(2, 2)))
model.add(Flatten())
model.add(Dense(512, activation='relu', kernel_constraint=maxnorm(3)))
model.add(Dropout(0.5))
model.add(Dense(num_classes, activation='softmax'))
# Compile model
epochs = 25
lrate = 0.01
decay = lrate/epochs
sgd = SGD(lr=lrate, momentum=0.9, decay=decay, nesterov=False)
model.compile(loss='categorical_crossentropy', optimizer=sgd, metrics=['accuracy'])
print(model.summary())
# Fit the model
model.fit(X_train, y_train, validation_data=(X_test, y_test), epochs=epochs, batch_size=32)
# Final evaluation of the model
scores = model.evaluate(X_test, y_test, verbose=0)
print("Accuracy: %.2f%%" % (scores[1]*100))
I receive the following error:
ValueError: Input 0 of layer sequential is incompatible with the layer: expected axis -1 of input shape to have value 32 but received input with shape [None, 32, 32, 3]
Just thrown off since no one else had this issue with the given code. I did have to change the first line from
K.set_image_dim_ordering('th')
to
K.image_data_format
as I was being told that set_image_dim_ordering was not a known function of Keras.backend
Any ideas here? Could my change has introduced this error?

Update: If data is in channel_last format, then change input shape from input_shape=(3, 32, 32) to input_shape=(32, 32, 3) in,
model.add(Conv2D(32, (3, 3), input_shape=(3, 32, 32), padding='same', activation='relu', kernel_constraint=maxnorm(3)))
I found reference to set_image_dim_ordering below for keras 1.2.2,
https://faroit.com/keras-docs/1.2.2/backend/
Here, it mentions,
For 2D data (e.g. image), "tf" assumes (rows, cols, channels) while
"th" assumes (channels, rows, cols).
Since you are receving input shape [None, 32, 32, 3] that means it is tf format of (rows, cols, channels). So providing th means the input shapes mismatch. You were using theano backend with th instead of tensorflow.
I could not find any reference to set_image_dim_ordering or image_dim_ordering in latest tf.keras backend. Instead these below provide method for getting and setting 'channels_first' or 'channels_last' format.
https://www.tensorflow.org/api_docs/python/tf/keras/backend/image_data_format
https://www.tensorflow.org/api_docs/python/tf/keras/backend/set_image_data_format

Keras - "ValueError: Error when checking target: expected activation_1 to have shape (None, 9) but got array with shape (9,1)

I'm building a model to classify text into one of 9 layers, and am having this error when running it. Activation 1 seems to refer to the Convolutional layer's input, but I'm unsure about what's wrong with the input.
num_classes=9
Y_train = keras.utils.to_categorical(Y_train, num_classes)
#Reshape data to add new dimension
X_train = X_train.reshape((100, 150, 1))
Y_train = Y_train.reshape((100, 9, 1))
model = Sequential()
model.add(Conv1d(1, kernel_size=3, activation='relu', input_shape=(None, 1)))
model.add(Dense(num_classes))
model.add(Activation('softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x=X_train,y=Y_train, epochs=200, batch_size=20)
Running this results in the following error:
"ValueError: Error when checking target: expected activation_1 to have shape (None, 9) but got array with shape (9,1)

There are several typos and bugs in your code.
Y_train = Y_train.reshape((100,9))
Since you reshape X_train to (100,150,1), I guess your input step is 150, and channel is 1. So for the Conv1D, (there is a typo in your code), input_shape=(150,1).
You need to flatten your output of conv1d before feeding into Dense layer.
import keras
from keras import Sequential
from keras.layers import Conv1D, Dense, Flatten
X_train = np.random.normal(size=(100,150))
Y_train = np.random.randint(0,9,size=100)
num_classes=9
Y_train = keras.utils.to_categorical(Y_train, num_classes)
#Reshape data to add new dimension
X_train = X_train.reshape((100, 150, 1))
Y_train = Y_train.reshape((100, 9))
model = Sequential()
model.add(Conv1D(2, kernel_size=3, activation='relu', input_shape=(150,1)))
model.add(Flatten())
model.add(Dense(num_classes, activation='softmax'))
model.compile(loss='categorical_crossentropy', optimizer='adam', metrics=['accuracy'])
model.fit(x=X_train,y=Y_train, epochs=200, batch_size=20)

Python - Tensorflow - LSTM- ValueError: Error when checking model target: expected dense_16 to have shape (None, 100) but got array with shape (16, 2)

Could someone help me to understand what this error is all about?
model = Sequential()
model.add(Embedding(82, 100, weights=[embedding_matrix], input_length=1000))
model.add(LSTM(100))
model.add(Dense(100, activation = 'sigmoid'))
model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy'])
print(model.summary())
model.fit(x_train, y_train, epochs = 5, batch_size=64)
When i run this LSTM model, I am getting an error as
ValueError: Error when checking model target: expected dense_16 to have shape (None, 100) but got array with shape (16, 2)
I am not sure how much the below information would be useful:
x_train.shape
Out[959]: (16, 1000)
y_train.shape
Out[962]: (16, 2)
If you need any other information, I am ready to provide

you have defined dense layer input shape is 100.
model.add(Dense(100, activation = 'sigmoid'))
so you need to make sure your input should always same shape.
here in your case make x_train and and y_train same shape.
try with :
model = Sequential()
# here the batch dimension is None,
# which means any batch size will be accepted by the model.
model.add(Dense(32, batch_input_shape=(None, 500)))
model.add(Dense(32))

Your last layer has an output shape of None,100
model.add(Dense(100, activation = 'sigmoid'))
But your data (y_train) has the shape (16,2). It should be
model.add(Dense(2, activation = 'sigmoid'))