python - 试图恢复张量流中完全连接的权重

saver = tf.train.import_meta_graph("my-model.meta") #saver.restore(sess,tf.train.latest_checkpoint('./')) saver.restore(sess, "my-model") graph=tf.get_default_graph() W1=graph.get_tensor_by_name("W1:0") W2=graph.get_tensor_by_name("W2:0") #X, Y = create_placeholders(240,480, 1, 2) X = graph.get_tensor_by_name("Placeholder:0") Y = graph.get_tensor_by_name("Placeholder_1:0") y_h1 = forward_propagation(X, W1, W2)

def forward_propagation(X, W1, W2): co1 = tf.nn.conv2d(X,W1, strides = [1,1,1,1], padding = 'SAME') A1 = tf.nn.relu(co1) po1 = tf.nn.max_pool(A1, ksize = [1,8,8,1], strides = [1,8,8,1], padding = 'SAME') co2 = tf.nn.conv2d(po1,W2, strides = [1,1,1,1], padding = 'SAME') A2 = tf.nn.relu(co2) po2 = tf.nn.max_pool(A2, ksize = [1,4,4,1], strides = [1,4,4,1], padding = 'SAME') P2_f = tf.contrib.layers.flatten(po2) fi = tf.contrib.layers.fully_connected(P2_f, 2, activation_fn=None) return fi

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'softmax_cross_entropy_with_logits_sg/concat/axis_2', 'softmax_cross_entropy_with_logits_sg/concat_3', 'softmax_cross_entropy_with_logits_sg/Reshape_4', 'softmax_cross_entropy_with_logits_sg/Rank_2_2', 'softmax_cross_entropy_with_logits_sg/Shape_2_2', 'softmax_cross_entropy_with_logits_sg/Sub_1/y_2', 'softmax_cross_entropy_with_logits_sg/Sub_1_2', 'softmax_cross_entropy_with_logits_sg/Slice_1/begin_2', 'softmax_cross_entropy_with_logits_sg/Slice_1/size_2', 'softmax_cross_entropy_with_logits_sg/Slice_1_2', 'softmax_cross_entropy_with_logits_sg/concat_1/values_0_2', 'softmax_cross_entropy_with_logits_sg/concat_1/axis_2', 'softmax_cross_entropy_with_logits_sg/concat_1_2', 'softmax_cross_entropy_with_logits_sg/Reshape_1_2', 'softmax_cross_entropy_with_logits_sg_2', 'softmax_cross_entropy_with_logits_sg/Sub_2/y_2', 'softmax_cross_entropy_with_logits_sg/Sub_2_2', 'softmax_cross_entropy_with_logits_sg/Slice_2/begin_2', 'softmax_cross_entropy_with_logits_sg/Slice_2/size_2', 'softmax_cross_entropy_with_logits_sg/Slice_2_2', 'softmax_cross_entropy_with_logits_sg/Reshape_2_2', 'gradients/Conv2D_1_grad/Conv2DBackpropInput_2', 'gradients/Conv2D_1_grad/Conv2DBackpropFilter_2', 'gradients/Conv2D_1_grad/tuple/group_deps_2', 'gradients/Conv2D_1_grad/tuple/control_dependency_3', 'gradients/Conv2D_1_grad/tuple/control_dependency_1_2', 'gradients/MaxPool_grad/MaxPoolGrad_2', 'gradients/Relu_grad/ReluGrad_2', 'gradients/Conv2D_grad/ShapeN_2', 'gradients/Conv2D_grad/Conv2DBackpropInput_2', 'gradients/Conv2D_grad/Conv2DBackpropFilter_2', 'gradients/Conv2D_grad/tuple/group_deps_2', 'gradients/Conv2D_grad/tuple/control_dependency_3', 'gradients/Conv2D_grad/tuple/control_dependency_1_2', 'beta1_power/initial_value_2', 'beta1_power_2', 'beta1_power/Assign_2', 'beta1_power/read_2', 'beta2_power/initial_value_2', 'beta2_power_2', 'beta2_power/Assign_2', 'beta2_power/read_2', 'W1/Adam/Initializer/zeros_2', 'W1/Adam_3', 'W1/Adam/Assign_2', 'W1/Adam/read_2', 'W1/Adam_1/Initializer/zeros_2', 'W1/Adam_1_2', 'W1/Adam_1/Assign_2', 'W1/Adam_1/read_2', 'W2/Adam/Initializer/zeros_2', 'W2/Adam_3', 'W2/Adam/Assign_2', 'W2/Adam/read_2', 'W2/Adam_1/Initializer/zeros_2', 'W2/Adam_1_2', 'W2/Adam_1/Assign_2', 'W2/Adam_1/read_2', 'fully_connected/weights/Adam/Initializer/zeros/shape_as_tensor_2', 'fully_connected/weights/Adam/Initializer/zeros/Const_2', 'fully_connected/weights/Adam/Initializer/zeros_2', 'fully_connected/weights/Adam_3', 'fully_connected/weights/Adam/Assign_2', 'fully_connected/weights/Adam/read_2', 'fully_connected/weights/Adam_1/Initializer/zeros/shape_as_tensor_2', 'fully_connected/weights/Adam_1/Initializer/zeros/Const_2', 'fully_connected/weights/Adam_1/Initializer/zeros_2', 'fully_connected/weights/Adam_1_2', 'fully_connected/weights/Adam_1/Assign_2', 'fully_connected/weights/Adam_1/read_2', 'fully_connected/biases/Adam/Initializer/zeros_2', 'fully_connected/biases/Adam_3', 'fully_connected/biases/Adam/Assign_2', 'fully_connected/biases/Adam/read_2', 'fully_connected/biases/Adam_1/Initializer/zeros_2', 'fully_connected/biases/Adam_1_2', 'fully_connected/biases/Adam_1/Assign_2', 'fully_connected/biases/Adam_1/read_2', 'Adam/learning_rate_2', 'Adam/beta1_2', 'Adam/beta2_2', 'Adam/epsilon_2', 'Adam/update_W1/ApplyAdam_2', 'Adam/update_W2/ApplyAdam_2', 'Adam/update_fully_connected/weights/ApplyAdam_2', 'Adam/update_fully_connected/biases/ApplyAdam_2', 'Adam/mul_3', 'Adam/Assign_3', 'Adam/mul_1_2', 'Adam/Assign_1_2', 'Adam_2', 'init_2']