embedding_vecor_length = 32 model = Sequential() model.add( Embedding(max_features, embeddings_dim, input_length=max_sent_len, mask_zero=False, weights=[embedding_weights])) model.add(Dropout(dropout_prob[1])) model.add(LSTM(100)) model.add(Dropout(dropout_prob[1])) if num_classes == 2: model.add(Dense(1, activation='sigmoid')) else: model.ad(Dense(num_classes, activation='sigmoid')) if num_classes == 2: model.compile(loss='binary_crossentropy', optimizer='Adagrad', metrics=['accuracy']) else: model.compile(loss='categorical_crossentropy', optimizer='Adagrad', metrics=['accuracy']) # model.compile(loss='binary_crossentropy', optimizer='adam', metrics=['accuracy']) print(model.summary()) # plot_model(model, to_file='C:\\Users\\paperspace\\Desktop\\Plots\\model_plot.png', show_shapes=True, show_layer_names=True) plot_model( model, to_file='C:\\Users\\paperspace\\Desktop\\Plots\\model_plot_LSTM_2.png',
#Initialising the CNN classifier = Sequential() #Step 1 - Convolution classifier.add( Convolution2D(32, 3, 3, input_shape(64, 64, 3), activation='relu')) #Step 2 - Pooling classifier.add(MaxPooling2D(pool_size=(2, 2))) #Step 3 - Flattening classifier.add(Flatten()) #Step 3 - Full Connection classifier.ad(Dense(units=128, activation='relu')) classifier.ad(Dense(units=1, activation='sigmoid')) #Compiling the CNN classifier.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) #Part 2 - Fitting the CNN to the images from keras.preprocessing.image import ImageDataGenerated train_datagen = ImageDataGenerator(rescale=1. / 255, shear_range=0.2, zoom_range=0.2, horizontal_flip=True) test_datagen = ImageDataGenerator(rescale=1. / 255)