question:As a prompt generator for a generative AI called "Midjourney", you will create image prompts for the AI to visualize. I will give you a concept, and you will provide a detailed prompt for Midjourney AI to generate an image. Please adhere to the structure and formatting below, and follow these guidelines: Do not use the words "description" or ":" in any form. Do not place a comma between [ar] and [v]. Write each prompt in one line without using return. Structure: [1] = 赛博朋克城市，俯视，大远景 [2] = a detailed description of [1] with specific imagery details. [3] = a detailed description of the scene's environment. [4] = a detailed description of the compositions. [5] = a detailed description of the scene's mood, feelings, and atmosphere. [6] = A style (e.g. photography, painting, illustration, sculpture, artwork, paperwork, 3D, etc.) for [1]. [7] = a detailed description of the scene's mood, feelings, and atmosphere. [ar] = Use "--ar 16:9" for horizontal images, "--ar 9:16" for vertical images, or "--ar 1:1" for square images. [v] = Use "--niji" for Japanese art style, or "--v 5" for other styles. Formatting: Follow this prompt structure: "/imagine prompt: [1], [2], [3], [4], [5], [6], [7], [ar] [v]". Your task: Create 4 distinct prompts for each concept [1], varying in details description, environment,compositions,atmosphere, and realization. Write your prompts in english. Do not describe unreal concepts as "real" or "photographic". Include one realistic photographic style prompt with lens type and size. Separate different prompts with two new lines. Example Prompts: /imagine prompt: cute dog, fluffy fur, wagging tail, playful expression, sitting on a grassy field, under a clear blue sky, with a colorful collar, in a natural and vibrant setting, by a lake, captured with a Nikon D750 camera, 50mm lens, shallow depth of field, composition focused on the dog's face, capturing its joyful spirit, in a style reminiscent of William Wegman's iconic dog portraits. --ar 1:1 --v 5.2 /imagine prompt: beautiful women in the coffee shop, elegant and sophisticated, sipping a cup of steaming coffee, natural sunlight streaming through the window, soft and warm color tones, vintage decor with cozy armchairs and wooden tables, a bookshelf filled with classic novels, delicate porcelain teacups, a hint of aromatic coffee beans in the air, captured by a Leica M10 camera, 35mm lens, capturing the essence of timeless beauty, composition focused on the woman's face and hands, reminiscent of a painting by Leonardo da Vinci. --ar 1:1 --v 5.2 /imagine prompt: A captivating Halo Reach landscape with a Spartan amidst a battlefield, fallen enemies around, smoke and fire in the background, emphasizing the Spartan's determination and bravery, detailed environment blending chaos and beauty, Illustration, digital art, --ar 16:9 --v 5

question:hi there

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question:Can you help fix my code? Error: Traceback (most recent call last): File "/home/pradyumnas/Desktop/Mahindra/Sem6/NN/updatedcud.py", line 149, in <module> val_loss = np.mean((A2_val.flatten() - y_val) ** 2) ~~~~~~~~~~~~~~~~~^~~~~~~ ValueError: operands could not be broadcast together with shapes (34444,) (8611,) Code: import numpy as np import pandas as pd import matplotlib.pyplot as plt import math from sklearn.model_selection import train_test_split from sklearn.preprocessing import MinMaxScaler from numba import cuda, float32 # Part 1: CPU-side Setup (Data Preparation and Basic Function Definitions) # Load the dataset data = pd.read_excel('/home/pradyumnas/Desktop/Mahindra/Sem6/NN/CCPP/Data.ods', sheet_name=None) full_data = pd.concat(data.values()) # Preprocess the data X = full_data.iloc[:, :-1].values y = full_data.iloc[:, -1].values # Normalize the data scaler_x = MinMaxScaler(feature_range=(-1, 1)) scaler_y = MinMaxScaler(feature_range=(-1, 1)) X_normalized = scaler_x.fit_transform(X) y_normalized = scaler_y.fit_transform(y.reshape(-1, 1)).flatten() # Split the data X_train, X_temp, y_train, y_temp = train_test_split(X_normalized, y_normalized, test_size=0.28, random_state=42) X_val, X_test, y_val, y_test = train_test_split(X_temp, y_temp, test_size=(10/28), random_state=42) # ANN architecture parameters n_input = X_train.shape[1] n_hidden = 10 # Example: 10 hidden nodes n_output = 1 learning_rate = 0.001 lambda_reg = 0.01 # Regularization parameter # Part 2: GPU-accelerated Computation Logic with Numba CUDA # CUDA Kernel for Forward Propagation @cuda.jit def forward_propagation_cuda(X, W1, b1, W2, b2, A1, A2): row = cuda.grid(1) if row < X.shape[0]: for j in range(W1.shape[1]): Z1_val = 0.0 for k in range(W1.shape[0]): Z1_val += X[row, k] * W1[k, j] A1[row, j] = math.tanh(Z1_val + b1[j]) for j in range(W2.shape[1]): Z2_val = 0.0 for k in range(W2.shape[0]): Z2_val += A1[row, k] * W2[k, j] A2[row, j] = 1 / (1 + math.exp(-(Z2_val + b2[j]))) # CUDA Kernel for Backward Propagation @cuda.jit def backward_propagation_cuda(X, Y, W1, W2, A1, A2, dW1, dW2, db1, db2, learning_rate): row = cuda.grid(1) if row < X.shape[0]: # Initialize dZ1 array with zeros dZ1_size = W1.shape[1] # Calculate the size based on the dimensions of W1 dZ1 = cuda.local.array(shape=(dZ1_size,), dtype=float32) for j in range(dZ1_size): dZ1[j] = 0.0 # Calculate gradients for W2 and b2 dZ2 = A2[row, 0] - Y[row] for j in range(W2.shape[1]): db2[j] += dZ2 for k in range(W2.shape[0]): dW2[k, j] += A1[row, k] * dZ2 # Backpropagate through hidden layer for j in range(W1.shape[1]): for k in range(W2.shape[0]): dZ1[j] += dZ2 * W2[k, j] # Update gradients for W1 and b1 for j in range(W1.shape[1]): db1[j] += dZ1[j] * (1 - A1[row, j]**2) for k in range(W1.shape[0]): dW1[k, j] += X[row, k] * dZ1[j] # Initialize GPU-focused parts # Allocate and copy forward propagation variables W1_device = cuda.to_device(np.random.randn(n_input, n_hidden).astype(np.float32)) b1_device = cuda.to_device(np.random.randn(n_hidden).astype(np.float32)) W2_device = cuda.to_device(np.random.randn(n_hidden, n_output).astype(np.float32)) b2_device = cuda.to_device(np.random.randn(n_output).astype(np.float32)) # Output arrays for hidden and output layers A1_device = cuda.device_array((X_train.shape[0], n_hidden), dtype=np.float32) A2_device = cuda.device_array((X_train.shape[0], n_output), dtype=np.float32) # Initializing arrays for gradients and parameter updates dW1_device = cuda.device_array_like(W1_device) dW2_device = cuda.device_array_like(W2_device) db1_device = cuda.device_array_like(b1_device) db2_device = cuda.device_array_like(b2_device) # Transfer training data to GPU X_train_device = cuda.to_device(X_train.astype(np.float32)) y_train_device = cuda.to_device(y_train.astype(np.float32)) # Validation data X_val_device = cuda.to_device(X_val.astype(np.float32)) y_val_device = cuda.to_device(y_val.astype(np.float32)) # Training loop execution on GPU num_epochs = 100 learning_rate = 0.001 train_losses = [] val_losses = [] for epoch in range(num_epochs): # Forward Propagation threadsperblock = 32 blockspergrid = (X_train.shape[0] + (threadsperblock - 1)) // threadsperblock forward_propagation_cuda, threadsperblock # Backward Propagation threadsperblock = 32 blockspergrid = (X_train.shape[0] + (threadsperblock - 1)) // threadsperblock backward_propagation_cuda, threadsperblock # Synchronize threads cuda.synchronize() # Copy gradients back to host for parameter updates dW1 = dW1_device.copy_to_host() dW2 = dW2_device.copy_to_host() db1 = db1_device.copy_to_host() db2 = db2_device.copy_to_host() # Update parameters dW1 -= learning_rate * (dW1 / X_train.shape[0]) dW2 -= learning_rate * (dW2 / X_train.shape[0]) db1 -= learning_rate * (db1 / X_train.shape[0]) db2 -= learning_rate * (db2 / X_train.shape[0]) # Validation Loss Calculation if epoch % 10 == 0: # Forward Propagation on Validation Data forward_propagation_cuda, threadsperblock # Copy result back to host for validation loss calculation A2_val = A2_device.copy_to_host() # Calculate validation loss (Mean Squared Error) val_loss = np.mean((A2_val.flatten() - y_val) ** 2) val_losses.append(val_loss) print(f"Epoch {epoch}: Validation Loss = {val_loss}") # Training Loss Calculation # Forward Propagation on Training Data forward_propagation_cuda, threadsperblock # Copy result back to host for training loss calculation A2_train = A2_device.copy_to_host() # Calculate training loss (Mean Squared Error) train_loss = np.mean((A2_train.flatten() - y_train) ** 2) train_losses.append(train_loss) print(f"Epoch {epoch}: Training Loss = {train_loss}") # Plotting Training and Validation Losses epochs = range(0, num_epochs, 10) plt.plot(epochs, train_losses, label='Training Loss') plt.plot(epochs, val_losses, label='Validation Loss') plt.xlabel('Epochs') plt.ylabel('Loss') plt.title('Training and Validation Losses') plt.legend() plt.show() # End of training loop # Copy final trained parameters back to host W1_final = W1_device.copy_to_host() b1_final = b1_device.copy_to_host() W2_final = W2_device.copy_to_host() b2_final = b2_device.copy_to_host() # Optionally, you can perform inference or further analysis using the trained parameters