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

Question

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.

Answer 1

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.