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[pre-commit.ci] auto fixes from pre-commit.com hooks
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@ -44,9 +44,11 @@ link : https://www.kaggle.com/code/navjindervirdee/lstm-neural-network-from-scra
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from tqdm import tqdm
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import numpy as np
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class LSTM:
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def __init__(self, data: str, hidden_dim: int = 25,
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epochs: int = 1000, lr: float = 0.05) -> None:
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def __init__(
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self, data: str, hidden_dim: int = 25, epochs: int = 1000, lr: float = 0.05
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) -> None:
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"""
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Initialize the LSTM network with the given data and hyperparameters.
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@ -63,7 +65,7 @@ class LSTM:
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self.chars = set(self.data)
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self.data_size, self.char_size = len(self.data), len(self.chars)
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print(f'Data size: {self.data_size}, Char Size: {self.char_size}')
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print(f"Data size: {self.data_size}, Char Size: {self.char_size}")
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self.char_to_idx = {c: i for i, c in enumerate(self.chars)}
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self.idx_to_char = {i: c for i, c in enumerate(self.chars)}
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@ -88,20 +90,16 @@ class LSTM:
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"""
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Initialize the weights and biases for the LSTM network.
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"""
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self.wf = self.init_weights(self.char_size + self.hidden_dim,
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self.hidden_dim)
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self.wf = self.init_weights(self.char_size + self.hidden_dim, self.hidden_dim)
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self.bf = np.zeros((self.hidden_dim, 1))
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self.wi = self.init_weights(self.char_size + self.hidden_dim,
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self.hidden_dim)
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self.wi = self.init_weights(self.char_size + self.hidden_dim, self.hidden_dim)
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self.bi = np.zeros((self.hidden_dim, 1))
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self.wc = self.init_weights(self.char_size + self.hidden_dim,
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self.hidden_dim)
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self.wc = self.init_weights(self.char_size + self.hidden_dim, self.hidden_dim)
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self.bc = np.zeros((self.hidden_dim, 1))
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self.wo = self.init_weights(self.char_size + self.hidden_dim,
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self.hidden_dim)
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self.wo = self.init_weights(self.char_size + self.hidden_dim, self.hidden_dim)
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self.bo = np.zeros((self.hidden_dim, 1))
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self.wy = self.init_weights(self.hidden_dim, self.char_size)
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@ -115,8 +113,9 @@ class LSTM:
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:param output_dim: The output dimension.
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:return: A matrix of initialized weights.
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"""
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return np.random.uniform(-1, 1, (output_dim, input_dim)) * \
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np.sqrt(6 / (input_dim + output_dim))
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return np.random.uniform(-1, 1, (output_dim, input_dim)) * np.sqrt(
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6 / (input_dim + output_dim)
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)
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##### Activation Functions #####
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def sigmoid(self, x: np.ndarray, derivative: bool = False) -> np.ndarray:
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@ -140,7 +139,7 @@ class LSTM:
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:return: The tanh activation or its derivative.
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"""
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if derivative:
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return 1 - x ** 2
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return 1 - x**2
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return np.tanh(x)
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def softmax(self, x: np.ndarray) -> np.ndarray:
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@ -182,21 +181,29 @@ class LSTM:
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outputs = []
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for t in range(len(inputs)):
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self.concat_inputs[t] = np.concatenate(
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(self.hidden_states[t - 1], inputs[t]))
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(self.hidden_states[t - 1], inputs[t])
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)
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self.forget_gates[t] = self.sigmoid(np.dot(self.wf,
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self.concat_inputs[t]) + self.bf)
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self.input_gates[t] = self.sigmoid(np.dot(self.wi,
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self.concat_inputs[t]) + self.bi)
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self.candidate_gates[t] = self.tanh(np.dot(self.wc,
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self.concat_inputs[t]) + self.bc)
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self.output_gates[t] = self.sigmoid(np.dot(self.wo,
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self.concat_inputs[t]) + self.bo)
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self.forget_gates[t] = self.sigmoid(
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np.dot(self.wf, self.concat_inputs[t]) + self.bf
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)
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self.input_gates[t] = self.sigmoid(
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np.dot(self.wi, self.concat_inputs[t]) + self.bi
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)
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self.candidate_gates[t] = self.tanh(
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np.dot(self.wc, self.concat_inputs[t]) + self.bc
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)
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self.output_gates[t] = self.sigmoid(
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np.dot(self.wo, self.concat_inputs[t]) + self.bo
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)
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self.cell_states[t] = self.forget_gates[t] * self.cell_states[t - 1] + \
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self.input_gates[t] * self.candidate_gates[t]
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self.hidden_states[t] = self.output_gates[t] * \
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self.tanh(self.cell_states[t])
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self.cell_states[t] = (
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self.forget_gates[t] * self.cell_states[t - 1]
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+ self.input_gates[t] * self.candidate_gates[t]
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)
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self.hidden_states[t] = self.output_gates[t] * self.tanh(
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self.cell_states[t]
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)
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outputs.append(np.dot(self.wy, self.hidden_states[t]) + self.by)
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@ -215,8 +222,10 @@ class LSTM:
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d_wo, d_bo = 0, 0
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d_wy, d_by = 0, 0
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dh_next, dc_next = np.zeros_like(self.hidden_states[0]), \
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np.zeros_like(self.cell_states[0])
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dh_next, dc_next = (
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np.zeros_like(self.hidden_states[0]),
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np.zeros_like(self.cell_states[0]),
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)
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for t in reversed(range(len(inputs))):
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error = errors[t]
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@ -228,45 +237,69 @@ class LSTM:
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d_hs = np.dot(self.wy.T, error) + dh_next
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# Output Gate Weights and Biases Errors
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d_o = self.tanh(self.cell_states[t]) * d_hs * \
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self.sigmoid(self.output_gates[t], derivative=True)
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d_o = (
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self.tanh(self.cell_states[t])
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* d_hs
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* self.sigmoid(self.output_gates[t], derivative=True)
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)
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d_wo += np.dot(d_o, inputs[t].T)
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d_bo += d_o
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# Cell State Error
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d_cs = self.tanh(self.tanh(self.cell_states[t]),
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derivative=True) * self.output_gates[t] * d_hs + dc_next
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d_cs = (
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self.tanh(self.tanh(self.cell_states[t]), derivative=True)
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* self.output_gates[t]
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* d_hs
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+ dc_next
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)
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# Forget Gate Weights and Biases Errors
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d_f = d_cs * self.cell_states[t - 1] * \
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self.sigmoid(self.forget_gates[t], derivative=True)
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d_f = (
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d_cs
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* self.cell_states[t - 1]
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* self.sigmoid(self.forget_gates[t], derivative=True)
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)
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d_wf += np.dot(d_f, inputs[t].T)
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d_bf += d_f
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# Input Gate Weights and Biases Errors
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d_i = d_cs * self.candidate_gates[t] * \
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self.sigmoid(self.input_gates[t], derivative=True)
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d_i = (
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d_cs
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* self.candidate_gates[t]
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* self.sigmoid(self.input_gates[t], derivative=True)
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)
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d_wi += np.dot(d_i, inputs[t].T)
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d_bi += d_i
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# Candidate Gate Weights and Biases Errors
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d_c = d_cs * self.input_gates[t] * self.tanh(self.candidate_gates[t],
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derivative=True)
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d_c = (
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d_cs
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* self.input_gates[t]
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* self.tanh(self.candidate_gates[t], derivative=True)
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)
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d_wc += np.dot(d_c, inputs[t].T)
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d_bc += d_c
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# Update the next hidden and cell state errors
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dh_next = np.dot(self.wf.T, d_f) + np.dot(self.wi.T, d_i) + \
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np.dot(self.wo.T, d_o) + np.dot(self.wc.T, d_c)
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dh_next = (
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np.dot(self.wf.T, d_f)
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+ np.dot(self.wi.T, d_i)
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+ np.dot(self.wo.T, d_o)
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+ np.dot(self.wc.T, d_c)
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)
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dc_next = d_cs * self.forget_gates[t]
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# Apply gradients to weights and biases
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for param, grad in zip([self.wf, self.wi, self.wc, self.wo, self.wy],
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[d_wf, d_wi, d_wc, d_wo, d_wy]):
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for param, grad in zip(
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[self.wf, self.wi, self.wc, self.wo, self.wy],
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[d_wf, d_wi, d_wc, d_wo, d_wy],
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):
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param -= self.lr * grad
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for param, grad in zip([self.bf, self.bi, self.bc, self.bo, self.by],
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[d_bf, d_bi, d_bc, d_bo, d_by]):
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for param, grad in zip(
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[self.bf, self.bi, self.bc, self.bo, self.by],
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[d_bf, d_bi, d_bc, d_bo, d_by],
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):
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param -= self.lr * grad
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def train(self) -> None:
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@ -301,11 +334,13 @@ class LSTM:
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Test the LSTM network on the input data and compute accuracy.
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"""
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inputs = [self.one_hot_encode(char) for char in self.train_X]
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correct_predictions = sum(self.idx_to_char[np.argmax(self.softmax(output))] == target
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for output, target in zip(self.forward(inputs), self.train_y))
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correct_predictions = sum(
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self.idx_to_char[np.argmax(self.softmax(output))] == target
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for output, target in zip(self.forward(inputs), self.train_y)
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)
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accuracy = (correct_predictions / len(self.train_y)) * 100
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print(f'Accuracy: {accuracy:.2f}%')
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print(f"Accuracy: {accuracy:.2f}%")
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if __name__ == "__main__":
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