bigger_model.py

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

Downloaded from strikingloo GitHub site