Update cnnrnn.py

cnnrnn.py

@@ -28,19 +28,17 @@ from tensorflow.keras.models import Model
 from tensorflow.keras.layers import Dense, LSTM, Input, Embedding, Conv2D, Concatenate, Flatten, Add, Dropout, GRU
 import random
 import datetime
-from nltk.translate.bleu_score import sentence_bleu
 
 
 
-def getModel(image):
-    embedding_matrix_vocab = np.load('my_embedding_matrix.npy')
 
-    input_shape = (image.shape[1],) 
-    text_input_shape = (153,)
-    input1 = Input(shape=input_shape, name='Image_input')
+def getModel():
+    
+    embedding_matrix_vocab = np.load('my_embedding_matrix.npy')
+    input1 = Input(shape=(2048,), name='Image_input')
     dense1 = Dense(256, kernel_initializer=tf.keras.initializers.glorot_uniform(seed = 56), name='dense_encoder')(input1)
 
-    input2 = Input(shape=text_input_shape, name='Text_Input')
+    input2 = Input(shape=(153,), name='Text_Input')
     embedding_layer = Embedding(input_dim = 1427, output_dim = 300, input_length=153, mask_zero=True, trainable=False,
                 weights=[embedding_matrix_vocab], name="Embedding_layer")
     emb = embedding_layer(input2)
@@ -71,9 +69,9 @@ def getModel(image):
 
     encoder_decoder = Model(inputs = [input1, input2], outputs = output)
 
-    encoder_decoder.load_weights("encoder_decoder_epoch_5.h5")
+    encoder_decoder.load_weights("/content/encoder_decoder_epoch_5.h5")
     # encoder
-  
+
     encoder_input = encoder_decoder.input[0]
     encoder_output = encoder_decoder.get_layer('dense_encoder').output
     encoder_model = Model(encoder_input, encoder_output)
@@ -89,13 +87,69 @@ def getModel(image):
     decoder_model = Model(inputs = [text_input, enc_output], outputs = decoder_output)
 
     return encoder_model,decoder_model
+    
+# def getModel(image):
+#     embedding_matrix_vocab = np.load('my_embedding_matrix.npy')
+
+#     input1 = Input(shape=(2048), name='Image_input')
+#     dense1 = Dense(256, kernel_initializer=tf.keras.initializers.glorot_uniform(seed = 56), name='dense_encoder')(input1)
+
+#     input2 = Input(shape=(153), name='Text_Input')
+#     embedding_layer = Embedding(input_dim = 1427, output_dim = 300, input_length=153, mask_zero=True, trainable=False,
+#                 weights=[embedding_matrix_vocab], name="Embedding_layer")
+#     emb = embedding_layer(input2)
+
+#     LSTM1 = LSTM(units=256, activation='tanh', recurrent_activation='sigmoid', use_bias=True,
+#             kernel_initializer=tf.keras.initializers.glorot_uniform(seed=23),
+#             recurrent_initializer=tf.keras.initializers.orthogonal(seed=7),
+#             bias_initializer=tf.keras.initializers.zeros(), return_sequences=True, name="LSTM1")(emb)
+#     #LSTM1_output = LSTM1(emb)
+
+#     LSTM2 = LSTM(units=256, activation='tanh', recurrent_activation='sigmoid', use_bias=True,
+#             kernel_initializer=tf.keras.initializers.glorot_uniform(seed=23),
+#             recurrent_initializer=tf.keras.initializers.orthogonal(seed=7),
+#             bias_initializer=tf.keras.initializers.zeros(), name="LSTM2")
+#     LSTM2_output = LSTM2(LSTM1)
+
+#     dropout1 = Dropout(0.5, name='dropout1')(LSTM2_output)
+
+#     dec =  tf.keras.layers.Add()([dense1, dropout1])
+
+#     fc1 = Dense(256, activation='relu', kernel_initializer=tf.keras.initializers.he_normal(seed = 63), name='fc1')
+#     fc1_output = fc1(dec)
+
+#     dropout2 = Dropout(0.4, name='dropout2')(fc1_output)
+
+#     output_layer = Dense(1427, activation='softmax', name='Output_layer')
+#     output = output_layer(dropout2)
+
+#     encoder_decoder = Model(inputs = [input1, input2], outputs = output)
+
+#     encoder_decoder.load_weights("encoder_decoder_epoch_5.h5")
+#     # encoder
+  
+#     encoder_input = encoder_decoder.input[0]
+#     encoder_output = encoder_decoder.get_layer('dense_encoder').output
+#     encoder_model = Model(encoder_input, encoder_output)
+
+#     # decoder#
+#     text_input = encoder_decoder.input[1]
+#     enc_output = Input(shape=(256,), name='Enc_Output')
+#     text_output = encoder_decoder.get_layer('LSTM2').output
+#     add1 = tf.keras.layers.Add()([text_output, enc_output])
+#     fc_1 = fc1(add1)
+#     decoder_output = output_layer(fc_1)
+
+#     decoder_model = Model(inputs = [text_input, enc_output], outputs = decoder_output)
+
+#     return encoder_model,decoder_model
 
 
 def greedysearch(image):
     # Open the pickle file for reading
     train_data = pd.read_csv('Final_Train_Data.csv')
     y_train = train_data['Report']
-    encoder_model, decoder_model = getModel(image)
+    encoder_model, decoder_model = getModel()
     input_ = 'startseq'
     image_features = encoder_model.predict(image)
     result = []