mirror of
https://github.com/MLSysBook/TinyTorch.git
synced 2026-04-28 21:32:33 -05:00
756d093920
- Implement complete capability showcase system (11 demonstrations) - Add auto-run showcases after successful module completion - Create interactive launcher for easy showcase navigation - Integrate with tito module complete workflow - Add user preference system for logo themes - Showcase student achievements without requiring additional work - Demonstrate real ML capabilities from tensors to TinyGPT - Use Rich terminal UI for beautiful visualizations
200 lines
6.9 KiB
Python
200 lines
6.9 KiB
Python
#!/usr/bin/env python3
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"""
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🚀 CAPABILITY SHOWCASE: Tensor Operations
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After Module 02 (Tensor)
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"Look what you built!" - Your tensors can do linear algebra!
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"""
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import sys
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import time
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from rich.console import Console
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from rich.panel import Panel
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from rich.table import Table
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from rich.progress import Progress, SpinnerColumn, TextColumn
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from rich.layout import Layout
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from rich.align import Align
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# Import from YOUR TinyTorch implementation
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try:
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from tinytorch.core.tensor import Tensor
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except ImportError:
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print("❌ TinyTorch not found. Make sure you've completed Module 02 (Tensor)!")
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sys.exit(1)
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console = Console()
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def ascii_matrix(matrix_data, title="Matrix"):
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"""Create ASCII visualization of a matrix."""
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table = Table(title=title, show_header=False, show_edge=False)
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# Add columns based on matrix width
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for _ in range(len(matrix_data[0])):
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table.add_column(justify="center", style="cyan")
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# Add rows
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for row in matrix_data:
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table.add_row(*[f"{val:6.2f}" for val in row])
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return table
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def demonstrate_tensor_creation():
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"""Show tensor creation and basic operations."""
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console.print(Panel.fit("📊 TENSOR CREATION", style="bold blue"))
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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console=console,
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) as progress:
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task = progress.add_task("Creating tensors with YOUR code...", total=None)
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time.sleep(1)
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# Create tensors using student's implementation
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a = Tensor([[1, 2, 3], [4, 5, 6]])
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b = Tensor([[7, 8], [9, 10], [11, 12]])
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progress.update(task, description="✅ Tensors created!")
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time.sleep(0.5)
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console.print("\n🎯 Matrix A:")
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console.print(ascii_matrix(a.data, "Your Tensor A"))
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console.print("\n🎯 Matrix B:")
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console.print(ascii_matrix(b.data, "Your Tensor B"))
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return a, b
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def demonstrate_matrix_multiplication(a, b):
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"""Show matrix multiplication with visual explanation."""
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console.print(Panel.fit("⚡ MATRIX MULTIPLICATION", style="bold green"))
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console.print("🧮 Computing A @ B using YOUR implementation...")
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with Progress(
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SpinnerColumn(),
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TextColumn("[progress.description]{task.description}"),
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console=console,
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) as progress:
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task = progress.add_task("Multiplying matrices...", total=None)
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time.sleep(1)
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# Use student's matrix multiplication
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result = a.matmul(b)
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progress.update(task, description="✅ Matrix multiplication complete!")
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time.sleep(0.5)
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console.print(f"\n🎯 Result Shape: {result.shape}")
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console.print("\n📊 A @ B =")
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console.print(ascii_matrix(result.data, "Matrix Multiplication Result"))
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# Show the math visually
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console.print("\n🔍 What happened:")
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console.print(" [1×7 + 2×9 + 3×11] [1×8 + 2×10 + 3×12]")
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console.print(" [4×7 + 5×9 + 6×11] [4×8 + 5×10 + 6×12]")
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console.print(" ↓ ↓")
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console.print(" [58] [64]")
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console.print(" [139] [154]")
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return result
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def demonstrate_tensor_operations():
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"""Show various tensor operations."""
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console.print(Panel.fit("🔧 TENSOR OPERATIONS", style="bold yellow"))
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# Create a simple tensor
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x = Tensor([[2, 4, 6], [8, 10, 12]])
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console.print("🎯 Original Tensor:")
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console.print(ascii_matrix(x.data, "Tensor X"))
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# Transpose - check if available
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try:
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console.print("\n🔄 Transpose:")
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x_t = x.T if hasattr(x, 'T') else Tensor(np.array(x.data).T.tolist())
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console.print(ascii_matrix(x_t.data, "X.T"))
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except:
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console.print("\n🔄 Transpose not yet implemented (coming soon!)")
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# Element-wise operations (if implemented)
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try:
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console.print("\n➕ Addition (X + 5):")
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x_plus = x.add(5)
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console.print(ascii_matrix(x_plus.data, "X + 5"))
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except:
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console.print("\n➕ Addition not yet implemented (coming in later modules!)")
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try:
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console.print("\n✖️ Multiplication (X * 2):")
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x_mul = x.multiply(2)
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console.print(ascii_matrix(x_mul.data, "X * 2"))
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except:
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console.print("\n✖️ Multiplication not yet implemented (coming in later modules!)")
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def show_neural_network_preview():
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"""Preview how tensors will be used in neural networks."""
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console.print(Panel.fit("🧠 NEURAL NETWORK PREVIEW", style="bold magenta"))
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console.print("🔮 Coming soon in your TinyTorch journey:")
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console.print(" 🎯 These tensors will become neural network weights")
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console.print(" 🎯 Matrix multiplication will compute layer outputs")
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console.print(" 🎯 You'll train networks to recognize images and text")
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console.print(" 🎯 Eventually you'll build GPT from scratch!")
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# Simple preview calculation
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weights = Tensor([[0.1, 0.2, 0.3], [0.4, 0.5, 0.6]])
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inputs = Tensor([[1], [2], [3]])
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console.print(f"\n🔍 Preview - Neural layer calculation:")
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console.print(" Weights @ Inputs = Layer Output")
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output = weights.matmul(inputs)
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console.print(f" Result shape: {output.shape}")
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console.print(" (This will make sense after Module 05!)")
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def main():
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"""Main showcase function."""
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console.clear()
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# Header
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layout = Layout()
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header = Panel.fit(
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"[bold cyan]🚀 CAPABILITY SHOWCASE: TENSOR OPERATIONS[/bold cyan]\n"
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"[yellow]After Module 02 (Tensor)[/yellow]\n\n"
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"[green]\"Look what you built!\" - Your tensors can do linear algebra![/green]",
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border_style="bright_blue"
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)
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console.print(Align.center(header))
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console.print()
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try:
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# Demonstrate tensor capabilities
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a, b = demonstrate_tensor_creation()
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console.print("\n" + "="*60)
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result = demonstrate_matrix_multiplication(a, b)
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console.print("\n" + "="*60)
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demonstrate_tensor_operations()
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console.print("\n" + "="*60)
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show_neural_network_preview()
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# Celebration
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console.print("\n" + "="*60)
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console.print(Panel.fit(
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"[bold green]🎉 CONGRATULATIONS! 🎉[/bold green]\n\n"
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"[cyan]Your Tensor class is the foundation of all machine learning![/cyan]\n"
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"[white]Every neural network, from simple classifiers to GPT,[/white]\n"
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"[white]starts with the tensor operations YOU just implemented.[/white]\n\n"
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"[yellow]Next up: Activations (Module 03) - Adding intelligence to your tensors![/yellow]",
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border_style="green"
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))
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except Exception as e:
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console.print(f"❌ Error running showcase: {e}")
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console.print("💡 Make sure you've completed Module 02 and your Tensor class works!")
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if __name__ == "__main__":
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main() |