| import os
|
| from typing import Optional
|
|
|
| import tensorflow as tf
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|
|
|
|
| def build_classifier(input_dim: int,
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| hidden: Optional[list[int]] = None,
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| dropout: float = 0.1,
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| use_batch_norm: bool = True,
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| use_residual: bool = True) -> tf.keras.Model:
|
| """Build an optimized binary classifier with BatchNorm and Residual connections.
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|
|
| Neurobook Chapter 6 optimizations:
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| - Batch Normalization: accelerates convergence, allows higher learning rates
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| - Residual connections: enables deeper networks without vanishing gradients
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|
|
| Args:
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| input_dim: number of input features
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| hidden: list of hidden layer sizes (default: [128, 64, 32])
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| dropout: dropout rate between dense layers
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| use_batch_norm: enable Batch Normalization (recommended)
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| use_residual: enable residual connections (recommended for deep nets)
|
| """
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| if hidden is None:
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| hidden = [128, 64, 32]
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|
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| inputs = tf.keras.Input(shape=(input_dim,), name="features")
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| x = inputs
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|
|
| # Initial projection if needed for residual
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| prev_units = input_dim
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|
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| for i, h in enumerate(hidden):
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| # Store input for residual connection
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| residual = x
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|
|
| # Dense layer with He initialization (Neurobook Chapter 1)
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| x = tf.keras.layers.Dense(
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| h,
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| activation=None, # Linear before BN
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| kernel_initializer='he_normal',
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| name=f"dense_{i}"
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| )(x)
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|
|
| # Batch Normalization (Neurobook Chapter 6)
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| if use_batch_norm:
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| x = tf.keras.layers.BatchNormalization(name=f"bn_{i}")(x)
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|
|
| # Activation after BN (best practice)
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| x = tf.keras.layers.Activation('relu', name=f"relu_{i}")(x)
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|
|
| # Dropout for regularization
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| if dropout and dropout > 0:
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| x = tf.keras.layers.Dropout(dropout, name=f"dropout_{i}")(x)
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|
|
| # Residual connection (Neurobook Chapter 6)
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| if use_residual and prev_units == h:
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| x = tf.keras.layers.Add(name=f"residual_{i}")([x, residual])
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|
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| prev_units = h
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|
|
| outputs = tf.keras.layers.Dense(1, activation='sigmoid', name="p_win")(x)
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|
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| model = tf.keras.Model(inputs=inputs, outputs=outputs, name="dualEA_optimized_classifier")
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|
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| # Higher learning rate possible with BatchNorm (Neurobook Chapter 6)
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| model.compile(
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| optimizer=tf.keras.optimizers.Adam(learning_rate=3e-3), # 3x higher with BN
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| loss='binary_crossentropy',
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| metrics=[
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| tf.keras.metrics.AUC(name="auc"),
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| tf.keras.metrics.BinaryAccuracy(name="acc"),
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| tf.keras.metrics.Precision(name="precision"),
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| tf.keras.metrics.Recall(name="recall")
|
| ]
|
| )
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| return model
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|
|
|
|
| def build_attention_lstm(input_dim: int,
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| seq_len: int = 30,
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| lstm_units: Optional[list[int]] = None,
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| attention_heads: int = 4,
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| dense: Optional[list[int]] = None,
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| dropout: float = 0.1,
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| use_bidirectional: bool = True) -> tf.keras.Model:
|
| """Build Attention-based LSTM with Bidirectional processing.
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|
|
| Neurobook Chapter 5 (Attention) + Chapter 4 (RNN) optimizations:
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| - Self-Attention: captures long-range dependencies better than LSTM alone
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| - Bidirectional: captures past AND future context
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| - 2-3x better pattern recognition for time series
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|
|
| Args:
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| input_dim: number of per-timestep features
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| seq_len: sequence length (timesteps)
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| lstm_units: list of LSTM hidden sizes (default: [128, 64])
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| attention_heads: number of attention heads (default: 4)
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| dense: list of dense layer sizes after LSTM
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| dropout: dropout rate applied after LSTM and dense layers
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| use_bidirectional: enable bidirectional processing (recommended)
|
| """
|
| if lstm_units is None:
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| lstm_units = [128, 64]
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| if dense is None:
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| dense = [32]
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|
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| inputs = tf.keras.Input(shape=(seq_len, input_dim), name="seq_features")
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| x = inputs
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|
|
| # Bidirectional LSTM layers (Neurobook Chapter 4)
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| for i, u in enumerate(lstm_units):
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| return_seq = True # Keep sequences for attention
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|
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| lstm_layer = tf.keras.layers.LSTM(
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| u,
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| return_sequences=return_seq,
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| kernel_initializer='he_normal',
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| name=f"lstm_{i}"
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| )
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|
|
| if use_bidirectional and i == 0: # Bidirectional on first layer
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| x = tf.keras.layers.Bidirectional(
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| lstm_layer,
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| name=f"bilstm_{i}"
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| )(x)
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| else:
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| x = lstm_layer(x)
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|
|
| if dropout and dropout > 0:
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| x = tf.keras.layers.Dropout(dropout, name=f"lstm_dropout_{i}")(x)
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|
|
| # Multi-Head Self-Attention (Neurobook Chapter 5)
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| # Captures which time steps are most relevant
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| attention_output = tf.keras.layers.MultiHeadAttention(
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| num_heads=attention_heads,
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| key_dim=lstm_units[-1] // attention_heads,
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| name="self_attention"
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| )(x, x)
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|
|
| # Global average pooling across time dimension
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| x = tf.keras.layers.GlobalAveragePooling1D(name="temporal_pooling")(attention_output)
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|
|
| # Dense layers with Batch Normalization
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| for j, h in enumerate(dense):
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| x = tf.keras.layers.Dense(h, activation=None, name=f"post_dense_{j}")(x)
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| x = tf.keras.layers.BatchNormalization(name=f"post_bn_{j}")(x)
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| x = tf.keras.layers.Activation('relu', name=f"post_relu_{j}")(x)
|
| if dropout and dropout > 0:
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| x = tf.keras.layers.Dropout(dropout, name=f"post_dropout_{j}")(x)
|
|
|
| outputs = tf.keras.layers.Dense(1, activation='sigmoid', name="p_win")(x)
|
|
|
| model = tf.keras.Model(inputs=inputs, outputs=outputs, name="dualEA_attention_lstm")
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| model.compile(
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| optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
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| loss='binary_crossentropy',
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| metrics=[
|
| tf.keras.metrics.AUC(name="auc"),
|
| tf.keras.metrics.BinaryAccuracy(name="acc"),
|
| tf.keras.metrics.Precision(name="precision"),
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| tf.keras.metrics.Recall(name="recall")
|
| ]
|
| )
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| return model
|
|
|
|
|
| def build_gru(input_dim: int,
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| seq_len: int = 30,
|
| gru_units: Optional[list[int]] = None,
|
| dense: Optional[list[int]] = None,
|
| dropout: float = 0.1) -> tf.keras.Model:
|
| """Build GRU-based model - 25-30% faster than LSTM with similar accuracy.
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|
|
| Neurobook Chapter 4: GRU is a lighter alternative to LSTM
|
| - Fewer parameters (no cell state)
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| - Faster training and inference
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| - Similar performance on most time series tasks
|
|
|
| Args:
|
| input_dim: number of per-timestep features
|
| seq_len: sequence length (timesteps)
|
| gru_units: list of GRU hidden sizes (default: [128, 64])
|
| dense: list of dense layer sizes after GRU
|
| dropout: dropout rate applied after GRU and dense layers
|
| """
|
| if gru_units is None:
|
| gru_units = [128, 64]
|
| if dense is None:
|
| dense = [32]
|
|
|
| inputs = tf.keras.Input(shape=(seq_len, input_dim), name="seq_features")
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| x = inputs
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|
|
| for i, u in enumerate(gru_units):
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| return_seq = (i < len(gru_units) - 1)
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|
|
| x = tf.keras.layers.GRU(
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| u,
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| return_sequences=return_seq,
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| kernel_initializer='he_normal',
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| name=f"gru_{i}"
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| )(x)
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|
|
| if dropout and dropout > 0:
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| x = tf.keras.layers.Dropout(dropout, name=f"gru_dropout_{i}")(x)
|
|
|
| # Dense layers with Batch Normalization
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| for j, h in enumerate(dense):
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| x = tf.keras.layers.Dense(h, activation=None, name=f"post_dense_{j}")(x)
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| x = tf.keras.layers.BatchNormalization(name=f"post_bn_{j}")(x)
|
| x = tf.keras.layers.Activation('relu', name=f"post_relu_{j}")(x)
|
| if dropout and dropout > 0:
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| x = tf.keras.layers.Dropout(dropout, name=f"post_dropout_{j}")(x)
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|
|
| outputs = tf.keras.layers.Dense(1, activation='sigmoid', name="p_win")(x)
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|
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| model = tf.keras.Model(inputs=inputs, outputs=outputs, name="dualEA_gru")
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| model.compile(
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| optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
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| loss='binary_crossentropy',
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| metrics=[
|
| tf.keras.metrics.AUC(name="auc"),
|
| tf.keras.metrics.BinaryAccuracy(name="acc")
|
| ]
|
| )
|
| return model
|
|
|
|
|
| def build_lstm(input_dim: int,
|
| seq_len: int = 30,
|
| lstm_units: Optional[list[int]] = None,
|
| dense: Optional[list[int]] = None,
|
| dropout: float = 0.1,
|
| use_batch_norm: bool = True) -> tf.keras.Model:
|
| """Build an optimized LSTM-based binary classifier.
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|
|
| Legacy wrapper with Neurobook Chapter 6 improvements (BatchNorm).
|
|
|
| Args:
|
| input_dim: number of per-timestep features
|
| seq_len: sequence length (timesteps)
|
| lstm_units: list of LSTM hidden sizes (default: [128, 64])
|
| dense: list of dense layer sizes after LSTM
|
| dropout: dropout rate applied after LSTM and dense layers
|
| use_batch_norm: enable Batch Normalization
|
| """
|
| if lstm_units is None:
|
| lstm_units = [128, 64]
|
| if dense is None:
|
| dense = [32]
|
|
|
| inputs = tf.keras.Input(shape=(seq_len, input_dim), name="seq_features")
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| x = inputs
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|
|
| for i, u in enumerate(lstm_units):
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| return_seq = (i < len(lstm_units) - 1)
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|
|
| x = tf.keras.layers.LSTM(
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| u,
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| return_sequences=return_seq,
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| kernel_initializer='he_normal',
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| name=f"lstm_{i}"
|
| )(x)
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|
|
| if dropout and dropout > 0:
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| x = tf.keras.layers.Dropout(dropout, name=f"lstm_dropout_{i}")(x)
|
|
|
| for j, h in enumerate(dense):
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| x = tf.keras.layers.Dense(h, activation=None, name=f"post_dense_{j}")(x)
|
| if use_batch_norm:
|
| x = tf.keras.layers.BatchNormalization(name=f"post_bn_{j}")(x)
|
| x = tf.keras.layers.Activation('relu', name=f"post_relu_{j}")(x)
|
| if dropout and dropout > 0:
|
| x = tf.keras.layers.Dropout(dropout, name=f"post_dropout_{j}")(x)
|
|
|
| outputs = tf.keras.layers.Dense(1, activation='sigmoid', name="p_win")(x)
|
|
|
| model = tf.keras.Model(inputs=inputs, outputs=outputs, name="dualEA_win_lstm")
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| model.compile(
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| optimizer=tf.keras.optimizers.Adam(learning_rate=1e-3),
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| loss='binary_crossentropy',
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| metrics=[
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| tf.keras.metrics.AUC(name="auc"),
|
| tf.keras.metrics.BinaryAccuracy(name="acc"),
|
| tf.keras.metrics.Precision(name="precision"),
|
| tf.keras.metrics.Recall(name="recall")
|
| ]
|
| )
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| return model
|
|
|
|
|
| def build_model(model_type: str = "classifier", **kwargs) -> tf.keras.Model:
|
| """Factory function to build any model type by name.
|
|
|
| Available models (per Neurobook recommendations):
|
| - "classifier": Fast feed-forward with BatchNorm + Residual (Chapter 6)
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| - "attention_lstm": Best accuracy with Self-Attention (Chapter 5)
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| - "gru": 25-30% faster than LSTM (Chapter 4)
|
| - "lstm": Optimized LSTM with BatchNorm (Chapter 6)
|
|
|
| Args:
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| model_type: one of ["classifier", "attention_lstm", "gru", "lstm"]
|
| **kwargs: passed to the specific builder function
|
|
|
| Returns:
|
| Compiled tf.keras.Model
|
| """
|
| builders = {
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| "classifier": build_classifier,
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| "attention_lstm": build_attention_lstm,
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| "gru": build_gru,
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| "lstm": build_lstm,
|
| }
|
|
|
| if model_type not in builders:
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| raise ValueError(f"Unknown model_type: {model_type}. Choose from {list(builders.keys())}")
|
|
|
| return builders[model_type](**kwargs)
|
|
|
|
|
| def export_to_onnx(model: tf.keras.Model, out_dir: str, opset: int = 13) -> str:
|
| """Export Keras model to ONNX format for MQL5 integration.
|
|
|
| Neurobook Chapter 3: ONNX Runtime integration for MQL5
|
| - Use opset 13+ for best compatibility
|
| - Dynamic batch size for inference flexibility
|
|
|
| Args:
|
| model: trained tf.keras.Model
|
| out_dir: output directory
|
| opset: ONNX opset version (default 13)
|
|
|
| Returns:
|
| Path to exported .onnx file
|
| """
|
| import tf2onnx
|
| import onnx
|
|
|
| os.makedirs(out_dir, exist_ok=True)
|
| onnx_path = os.path.join(out_dir, "model.onnx")
|
|
|
| # Convert to ONNX
|
| spec = (tf.TensorSpec((None,) + model.input_shape[1:], tf.float32, name="input"),)
|
| model_proto, _ = tf2onnx.convert.from_keras(model, input_signature=spec, opset=opset)
|
|
|
| # Save
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| onnx.save(model_proto, onnx_path)
|
|
|
| return onnx_path
|
|
|
|
|
| def save_model(model: tf.keras.Model, out_dir: str) -> str:
|
| os.makedirs(out_dir, exist_ok=True)
|
| path = os.path.join(out_dir, "tf_model.keras")
|
| model.save(path)
|
| return path
|
|
|
|
|
| def load_model(model_dir: str) -> tf.keras.Model:
|
| path = os.path.join(model_dir, "tf_model.keras")
|
| return tf.keras.models.load_model(path)
|
|
|
