Exam 16: Deep Learning  

Consider the output of the following output produced by calling a Keras model's summary method: _________________________________________________________________ Layer (type) Output Shape Param # ================================================================= Conv2d_1 (Conv2D) (None, 26, 26, 64) 640 _________________________________________________________________ Max_pooling2d_1 (MaxPooling2 (None, 13, 13, 64) 0 _________________________________________________________________ Conv2d_2 (Conv2D) (None, 11, 11, 128) 73856 _________________________________________________________________ Max_pooling2d_2 (MaxPooling2 (None, 5, 5, 128) 0 _________________________________________________________________ Flatten_1 (Flatten) (None, 3200) 0 _________________________________________________________________ Dense_1 (Dense) (None, 128) 409728 _________________________________________________________________ Dense_2 (Dense) (None, 10) 1290 ================================================================= Total params: 485,514 Trainable params: 485,514 Non-trainable params: 0 _________________________________________________________________ Which of the following statements is false?

A ________ function produces a measure of how well a neural network predicts the target values.

Which of the following statements about the IMDb movie reviews dataset a), b) or c) is false?

Which of the following statements is false?

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

A typical convolutional neural network consists of several layers-an xe "input layer"input layer that receives the training samples, ________ layers that learn from the samples and an xe "output layer"output layer that produces the prediction probabilities.

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

A tensor's ________ typically is represented in Python as a tuple of values in which the number of elements specifies the tensor's number of dimensions and each value in the tuple specifies the size of the tensor's corresponding dimension.

Which of the following statements is false?

Which of the following statements a), b) or c) is false?

Which of the following statements a), b) or c) is false?

Chollet discusses the types of tensors typically encountered in deep learning: $\bullet$ A 0D (0-dimensional) tensor is one value and is known as a xe "scalar value"scalar. $\bullet$ A 1D tensor is similar to a one-dimensional array and is known as a xe "vector"vector. A 1D tensor might represent a sequence, such as hourly temperature readings from a sensor or the words of one movie review. $\bullet$ A 2D tensor is similar to a two-dimensional array and is known as a xe "matrix"matrix. A 2D tensor could represent a grayscale image in which the tensor's two dimensions are the image's width and height in pixels, and the value in each element is the intensity of that pixel. Which of the following statements a), b) or c) about additional types of tensors is false?

Which of the following are popular deep learning applications?

Which of the following statements a), b) or c) about convolution is false?

Which of the following statements is false?

Consider the following code, which evaluates our convnet model using the MNIST test data: [38]: loss, accuracy = cnn.evaluate(X_test, y_test) 10000/10000 [==============================] - 4s 366us/step [39]: loss [39]: 0.026809450998473768 [40]: accuracy [40]: 0.9917 Which of the following statements a), b) or c) is false?

Which of the following statements is false?