CNN can now load weights from nn.h5 file

nns/Cnn_test.py

 import tensorflow as tf
 from tensorflow.keras.preprocessing.sequence import pad_sequences
 from tensorflow.keras import models, layers
+from tensorflow.keras.models import load_model
+
 # from tensorflow.keras.metrics import Accuracy, Precision, Recall
 import numpy as np
 from helper_functions import clean_classifications
@@ -15,9 +17,7 @@ class NeuralNetwork(object):
     :type target: str
     :param kernel_size: Kernel size of CNN
     :type kernel_size: int
-    :param weights: Initial weights to use for the neural network
-    :param max_sequence_length: Length to which pad or truncate the sequences
-    :type max_sequence_length: int
+    :param weights: Path to h5 file that contains model weights, optional
     :param batch_size: Batch size to use during training
     :param threshold: Assign label to TRUE if probability above this threshold
                       when doing prediction
@@ -45,17 +45,16 @@ class NeuralNetwork(object):
         self.model = models.Sequential()
 
         self.initialize(kwargs['weights'])
-        self.history = {'loss':[], 'binary_accuracy': [], 'precision': [],
+        self.history = {'loss': [], 'binary_accuracy': [], 'precision': [],
                         'recall': [], 'val_loss': [], 'val_binary_accuracy': [],
                         'val_precision': [], 'val_recall': []}
 
-    def initialize(self, weights):
-        """
-        Initialize the network. If weights are provided, these should be loaded.
+    def initialize(self, weights=None):
+        """Initialize the network.
+
+        :param weights: Path to .h5 model summary with weights, optional.
+                        If provided, use this to set the model weights
         """
-        if weights:
-            raise NotImplementedError('Cannot provide weights now,'
-                                      ' still needs to be implemented')
         # First layer
         self.model.add(layers.Conv1D(self.filters,
                                      kernel_size=self.kernel_size,
@@ -77,8 +76,12 @@ class NeuralNetwork(object):
         self.model.compile(optimizer=tf.keras.optimizers.Adam(learning_rate=self.learning_rate),
                            loss=tf.keras.losses.BinaryCrossentropy(from_logits=True),
                            metrics=[binary_accuracy, precision, recall])
-        # Uncomment to print model summary
-        print(self.model.summary())
+        if weights:
+            self.model.load_weights(weights)
+            print('Successfully loaded weights')
+
+        # # Uncomment to print model summary
+        # self.model.summary()
 
     def train(self, x, y, x_val, y_val, quiet=False, eps_per_kmer_switch=100):
         """Train the network. x_val/y_val may be used for validation/early