Prompt Title: Handwritten-Recognizing

class CTCLayer(keras.layers.Layer): def __init__(self, name=None): super().__init__(name=name) self.loss_fn = keras.backend.ctc_batch_cost def call(self, y_true, y_pred): batch_len = tf.cast(tf.shape(y_true)[0], dtype="int64") input_length = tf.cast(tf.shape(y_pred)[1], dtype="int64") label_length = tf.cast(tf.shape(y_true)[1], dtype="int64") input_length = input_length * tf.ones(shape=(batch_len, 1), dtype="int64") label_length = label_length * tf.ones(shape=(batch_len, 1), dtype="int64") loss = self.loss_fn(y_true, y_pred, input_length, label_length) self.add_loss(loss) # At test time, just return the computed predictions. return y_pred def build_model(): # Inputs to the model input_img = keras.Input(shape=(image_width, image_height, 1), name="image") labels = keras.layers.Input(name="label", shape=(None,)) # First conv block. x = keras.layers.Conv2D( 32, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same", name="Conv1", )(input_img) x = keras.layers.MaxPooling2D((2, 2), name="pool1")(x) # Second conv block. x = keras.layers.Conv2D( 64, (3, 3), activation="relu", kernel_initializer="he_normal", padding="same", name="Conv2", )(x) x = keras.layers.MaxPooling2D((2, 2), name="pool2")(x) # We have used two max pool with pool size and strides 2. # Hence, downsampled feature maps are 4x smaller. The number of # filters in the last layer is 64. Reshape accordingly before # passing the output to the RNN part of the model. new_shape = ((image_width // 4), (image_height // 4) * 64) x = keras.layers.Reshape(target_shape=new_shape, name="reshape")(x) x = keras.layers.Dense(64, activation="relu", name="dense1")(x) x = keras.layers.Dropout(0.2)(x) # RNNs. x = keras.layers.Bidirectional( keras.layers.LSTM(128, return_sequences=True, dropout=0.25) )(x) x = keras.layers.Bidirectional( keras.layers.LSTM(64, return_sequences=True, dropout=0.25) )(x) # +2 is to account for the two special tokens introduced by the CTC loss. # The recommendation comes here: https://git.io/J0eXP. x = keras.layers.Dense( len(char_to_num.get_vocabulary()) + 2, activation="softmax", name="dense2" )(x) # Add CTC layer for calculating CTC loss at each step. output = CTCLayer(name="ctc_loss")(labels, x) # Define the model. model = keras.models.Model( inputs=[input_img, labels], outputs=output, name="handwriting_recognizer" ) # Optimizer. opt = keras.optimizers.Adam() # Compile the model and return. model.compile(optimizer=opt) return model # Get the model. model = build_model() model.summary()