fc_layer_size = 256 img_size = IMG_SIZE conv_inputs = keras.Input(shape=(img_size[1], img_size[0],3), name='ani_image') #first convolutional layer. conv_layer = layers.Conv2D(48, kernel_size=3, activation='relu')(conv_inputs) conv_layer = layers.MaxPool2D(pool_size=(2,2))(conv_layer) #second convolutional layer. conv_layer = layers.Conv2D(48, kernel_size=3, activation='relu')(conv_layer) conv_layer = layers.MaxPool2D(pool_size=(2,2))(conv_layer) conv_x = layers.Flatten(name = 'flattened_features')(conv_layer) #turn image to vector. conv_x = layers.Dense(fc_layer_size, activation='relu', name='first_layer')(conv_x) conv_x = layers.Dense(fc_layer_size, activation='relu', name='second_layer')(conv_x) conv_outputs = layers.Dense(1, activation='sigmoid', name='class')(conv_x) conv_model = keras.Model(inputs=conv_inputs, outputs=conv_outputs) #Epoch 15/15 #4096/4096 [==============================] - 154s 38ms/sample - #loss: 0.8806 - binary_crossentropy: 0.8806 - mean_squared_error: 0.2402 #val_loss: 1.5379 - val_binary_crossentropy: 1.5379 - val_mean_squared_error: 0.3302 #Labels vs predictions correlation coefficient 0.2188213