Discrepancy in Predictions

Hi, I am fairly new to TensorFlow and trying to figure out a few things.
For Q1, I have a matrix of dimensions 46 and 5, representing 5 feature vectors. I have defined two models: the first one is the basic neural network structure, and the second one uses a convolutional layer. I don’t have a very good understanding of the input layer and size. So, I need some feedback to confirm if the code is correct.

X_train = np.random.rand(44, 5)
y_train = np.random.rand(44, 1)
X_rem = np.random.rand(11, 5)
X_train= np.asarray(X_train).astype(np.float32)
X_rem= np.asarray(X_rem).astype(np.float32)
y_train= np.asarray(y_train).astype(np.float32)
train_dataset = tf.data.Dataset.from_tensor_slices((X_train, y_train))
train_dataset = train_dataset.batch(BATCH_SIZE)
test_dataset = test_dataset.batch(BATCH_SIZE)

model1 = tf.keras.Sequential([
tf.keras.layers.Dense(10, activation=‘relu’),
#tf.keras.layers.Dense(5, activation=‘relu’),

model2 = tf.keras.Sequential([
tf.keras.layers.Reshape((5, 1)),
tf.keras.layers.Conv1D(1, kernel_size=1, activation=‘relu’),
tf.keras.layers.Dense(12, activation=‘linear’),


Q2: If the code is correct, I have a matrix of dimensions (44, 5). Then, why is the input shape specified as (5,)? I understand that the model expects the input shape in the format (Batch size, height, width). However, I haven’t reshaped them before.
Q3: When using model1 , I am obtaining different results (model.predict(X_rem) ) for model.fit(X_train, y_train, epochs=100) and model.fit(train_dataset, epochs=100) . The output I get from model.fit(train_dataset, epochs=100) is a single value for all predictions."
Q4: If I using model2, I am getting a single value for all the predictions.

Let’s address your questions one by one, focusing on TensorFlow’s use and model structures.

Q1: Model Code Review

Your code for defining the models seems mostly correct, but there are a few points to consider:

  • For model1, you’re using a Flatten layer as the first layer, which is unnecessary since your input is already a 2D array (not a higher-dimensional tensor that needs flattening). You can directly start with a Dense layer.
  • For model2, you’re using a Conv1D layer, which is suitable for sequence or time-series data. The Reshape layer is correctly reshaping your 2D input into a 3D tensor to match the Conv1D input requirements. However, ensure that this model architecture aligns with your problem’s nature.
  • It looks like there might be a typo or copy-paste error since you only show model.compile but don’t specify which model (model1 or model2) you’re compiling. Ensure you compile both models separately if you plan to use both.

Q2: Input Shape Clarification

The input shape for your models is specified as (5,) because each instance in your dataset is a 1D array with 5 elements (your 5 features). TensorFlow models abstract away the batch size during model definition, so you only need to specify the shape of each individual sample. In your case, each sample has 5 features, hence the input shape (5,).

Q3: Discrepancy in Predictions

The discrepancy between model.fit(X_train, y_train, epochs=100) and model.fit(train_dataset, epochs=100) could be due to how the data is batched or processed in each approach. When you use tf.data.Dataset, it might introduce differences in data shuffling or batching, especially if your BATCH_SIZE does not divide your dataset size evenly.

  • Ensure consistent data preprocessing and batching between the two approaches. Check if you’re shuffling the data and if the batch sizes are the same in both cases.
  • The learning process can be stochastic, and slight differences in data handling can lead to different results. It’s also possible that using the entire dataset as one batch (since your batch size equals your dataset size) might be affecting the training dynamics.

Q4: Single Value for All Predictions

Getting a single value for all predictions might indicate that the model is not learning the underlying patterns in the data effectively and is instead converging to a mean or default prediction. This can happen due to several reasons:

  • Model Architecture: The model might be too simple or not suitable for the complexity of the task.
  • Overfitting/Underfitting: The model might be overfitting to a subset of the training data or underfitting due to insufficient capacity.
  • Learning Rate: A learning rate that’s too high or too low can hinder effective learning.
  • Data Issues: There might be issues with the data that make learning difficult, such as lack of variability or informative features.

To address these issues, consider:

  • Experimenting with different model architectures and complexities.
  • Adjusting the learning rate or using a learning rate scheduler.
  • Ensuring your data is properly preprocessed and representative of the problem space.
  • Employing regularization techniques or dropout to combat overfitting.

Remember, debugging machine learning models often involves iteratively adjusting the model, the data preprocessing, and the training process until you find a configuration that works well for your specific task.