mirror of
https://github.com/MLSysBook/TinyTorch.git
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86b908fe5c
- Create professional examples directory showcasing TinyTorch as real ML framework - Add examples: XOR, MNIST, CIFAR-10, text generation, autograd demo, optimizer comparison - Fix import paths in exported modules (training.py, dense.py) - Update training module with autograd integration for loss functions - Add progressive integration tests for all 16 modules - Document framework capabilities and usage patterns This commit establishes the examples gallery that demonstrates TinyTorch works like PyTorch/TensorFlow, validating the complete framework.
116 lines
3.5 KiB
Python
116 lines
3.5 KiB
Python
"""
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Tests for Module 05: Dense/Networks
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"""
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import pytest
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import numpy as np
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import sys
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from pathlib import Path
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# Add project root to path
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sys.path.insert(0, str(Path(__file__).parent.parent.parent))
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class TestDenseExports:
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"""Test that Dense layer is properly exported."""
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def test_dense_import(self):
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"""Test Dense can be imported from correct location."""
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from tinytorch.core.layers import Dense
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assert Dense is not None
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def test_dense_creation(self):
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"""Test Dense layer can be created."""
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from tinytorch.core.layers import Dense
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layer = Dense(10, 5)
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assert layer.weights.shape == (10, 5)
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class TestDenseForward:
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"""Test Dense layer forward pass."""
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def test_forward_shape(self):
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"""Test output shape is correct."""
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from tinytorch.core.layers import Dense
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from tinytorch.core.tensor import Tensor
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layer = Dense(10, 5)
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x = Tensor(np.random.randn(32, 10))
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output = layer(x)
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assert output.shape == (32, 5)
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def test_forward_with_bias(self):
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"""Test forward pass with bias."""
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from tinytorch.core.layers import Dense
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from tinytorch.core.tensor import Tensor
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layer = Dense(10, 5, use_bias=True)
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x = Tensor(np.zeros((1, 10)))
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output = layer(x)
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# With zero input, output should equal bias
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assert np.allclose(output.data, layer.bias.data)
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def test_forward_without_bias(self):
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"""Test forward pass without bias."""
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from tinytorch.core.layers import Dense
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from tinytorch.core.tensor import Tensor
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layer = Dense(10, 5, use_bias=False)
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x = Tensor(np.zeros((1, 10)))
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output = layer(x)
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# With zero input and no bias, output should be zero
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assert np.allclose(output.data, 0)
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class TestDenseIntegration:
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"""Test Dense layer integration with other modules."""
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def test_dense_with_tensor(self):
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"""Test Dense works with Tensor (Module 02)."""
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from tinytorch.core.layers import Dense
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from tinytorch.core.tensor import Tensor
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layer = Dense(10, 5)
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# Weights and bias should be Tensors
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assert isinstance(layer.weights, Tensor)
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if layer.use_bias:
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assert isinstance(layer.bias, Tensor)
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def test_dense_with_activations(self):
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"""Test Dense works with activations (Module 03)."""
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from tinytorch.core.layers import Dense
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from tinytorch.core.activations import ReLU, Sigmoid
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from tinytorch.core.tensor import Tensor
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layer = Dense(10, 5)
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relu = ReLU()
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sigmoid = Sigmoid()
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x = Tensor(np.random.randn(16, 10))
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h = layer(x)
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h_relu = relu(h)
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h_sigmoid = sigmoid(h)
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assert h_relu.shape == h.shape
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assert h_sigmoid.shape == h.shape
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assert np.all(h_sigmoid.data >= 0) and np.all(h_sigmoid.data <= 1)
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def test_dense_chain(self):
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"""Test chaining multiple Dense layers."""
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from tinytorch.core.layers import Dense
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from tinytorch.core.tensor import Tensor
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layer1 = Dense(784, 128)
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layer2 = Dense(128, 64)
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layer3 = Dense(64, 10)
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x = Tensor(np.random.randn(32, 784))
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h1 = layer1(x)
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h2 = layer2(h1)
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output = layer3(h2)
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assert output.shape == (32, 10) |